3 2 0 4

LIMITS AND CONTINUITY: Standard functions – Graphs - Limit- continuity- piecewise continuity- periodic- differentiable functions - Riemann sum- integrable functions- fundamental theorem of calculus. (4+4)

SEQUENCES & SERIES: Sequences – increasing- decreasing- bounded- function limit properties - Series – convergence and divergence – alternating series test- absolute convergence – ratio test- power series- Taylor series (single variable). (8+6)

FUNCTIONS OF TWO VARIABLES: Models- partial derivative and its geometrical interpretation- Stationary points – maxima and minima- saddle points- Taylor series- Constrained maxima and minima – Lagrange multiplier method. (6+4)

MULTIPLE INTEGRALS: Evaluation of multiple integrals – Cartesian and polar forms- Change of order of integration - Applications of multiple integrals to find area and volume. (9+6)

ORDINARY DIFFERENTIAL EQUATIONS:Linear Differential Equations of first order - Exact differential equations- Integrating factors- Bernoulli equations -Linear Differential Equations of higher order with constant coefficients -Euler's equation with variable coefficients - Simultaneous equations - Method of variation of parameters. Modeling simple systems.(12+8)

VECTOR CALCULUS:Vector differentiation-gradient- divergent- curl- vector integration- Greens theorem- Stokes theorem- Gauss divergence theorem (6+2)

Total L:45+T:30=75

TEXT BOOKS:
1. Thomas G B Jr., Maurice D Wier, Joel Hass, Frank R. Giordano, “Thomas’ Calculus”, Pearson Education, 2022.
2. Erwin Kreyszig, “Advanced Engineering Mathematics”,Wiley, 2018.

REFERENCES:
1. Ben Orlin, “Change Is the Only Constant: The Wisdom of Calculus in a Madcap World”, Black Dog & Leventhal,2019.
2. Ken F. Riley, Mike P. Hobson, Stephen J. Bence, “Mathematical Methods for Physics and Engineering”, Cambridge University Press, 2018
3. Deborah Hughes-Hallett , Patti Frazer Lock , Andrew M. Gleason,” Applied Calculus”, Wiley, 2022
4. Judith A. Beecher, Judith A. Penna, Marvin L. Bittinger, “College Algebra”, Pearson, 2021.

3 2 0 4

INTRODUCTION: Evolution of Life, Cells and Tissues – types and functions, Biological Molecules – Proteins, Nucleic Acids(10)

BASICS OF GENETICS AND MOLECULAR BIOLOGY: DNA, Genes, Chromosome, Traits, Mutations, Central Dogma, Transcription, Translation, RNAs. Basic Sequence Analysis using Bioinformatics Tools.(10)

GEONOMIC AND PROTEOMICS: Genomics: Genome, Human Genome Project, Sequencing, Gene Expression Studies. Proteomics: Amino acids and Proteins, Proteomics Data.(13)

BIOLOGICAL DATA: Types of Data, Sources, Data Formats, Biological databases – NCBI, ENSEMBL, GEO, Array Express, Peptide Atlas, The Human Protein Atlas, GDC, KEGG(12)

TUTORIAL PRACTICES: CASE STUDIES: Data related to Human Diseases – Databases and data retrieval.(30)

Total L: 45+T=30=75

TEXT BOOKS:
1. Peter H.Raven, George B.Johnson, Jonathan B. Losus, Susan R. Singer, “Biology”, WBC McGraw Hill, 2017.
2. Eldon D Enger, Frederick C Ross and David B Bailey, “Concepts in Biology”, Tata Mc-Graw Hill, , 2009

REFERENCES:
1. Gil Brum, Larry Mckane and Gerry Karp, “Biology fundamentals”, John-Wiley &Sons, 1995.
2. Julian Sutton, “Biology-Foundations”, Macmillan Company, London, 2002.

4 0 0 4

NUMBER SYSTEMS: Review of number systems-representation-conversions, error detection and error correction. Representation of unsigned and signed numbers – arithmetic operation on signed numbers -Representation of integer data -floating point representation of real data-IEEE representation. (11)

BOOLEAN ALGEBRA AND SIMPLICATION TECHNIQUES: Review of Boolean algebra - theorems, sum of product and product of sum simplification, canonical forms-minterm and maxterm, Simplification of Boolean expressions-Karnaugh map, completely and incompletely specified functions of Boolean expressions - Quine-McCluskey -NAND-NAND realizations. (11)

COMBINATIONAL LOGIC CIRCUITS: adders, subtractor’s, BCD adder, ripple carry look ahead adders, parity generator, decoders, encoders, multiplexers, demultiplexers, Realization of Boolean expressions- using decoders-using multiplexers, PAL and PLA logics. (11)

SEQUENTIAL CIRCUITS: latches, flip flops, edge triggering, asynchronous inputs. Shift registers, Universal shift register, applications. Binary counters – Synchronous and asynchronous up/down counters, mod-N counter, Counters for random sequence. (11)

SYNCHRONOUS CIRCUIT ANALYSIS: structure and operation, analysis-transition equations, state tables and state diagrams, Modelling- Moore machine and Mealy machine- serial binary adder, sequence recognizer, state table reduction, state assignment. Hazard; Overview and comparison of logic families. (11)

SIGNALS AND DATA: Representation of Analog and Digital signals – Digital /Analog data to Digital/Analog signals conversion Shift Keying techniques (5)

Total L:60

TEXT BOOKS:
1. D.P. Leach, A. P. Malvino, GoutamGuha, “Digital Principles and Applications”, Tata Mc-Graw Hill, 2011.
2. Charles H. Roth, Jr, Larry L.Kinney, “Fundamentals of Logic Design”, Cengage Learning, 2014.
3.Wakerly J F, “Digital Design: Principles and Practices, Prentice-Hall”, 2002.

REFERENCES:
1.M. M. Mano, “Digital Design”, Pearson Education, 2003.
2. D. D. Givone, “Digital Principles and Design”, Tata Mc-Graw Hill, 2003.
3. R.J.Tocci and N.S.Widner, “Digital Systems - Principles& Applications”, PHI, 2007.

4 0 0 4

PROBLEM SOLVING: Introduction to Problem Solving- Program development- Analyzing and Defining the Problem- Algorithm-Flow Chart - Programming languages-Types of programming languages- Program Development Environment.(5)

C LANGUAGE:Introduction to C Language - C Character Set - Identifiers and Keywords - Data Types – Literal Constants - Variables – l-value-r-value - Qualifiers – Modifiers - Operators and Expressions – Type conversions - Library Functions - Data Input and Output Functions – escape sequence characters – Formatted input and output.(6)

CONTROL STATEMENTS: Making Decisions : If Statement – If/else Statement - If/else if Statement – Nested if Statements – dangling else - Switch Multiple Selection Statement– Repetition : Repetition Essentials - While Loop – do-While Loop – For Loop – Nested Loops – Breaking out of a Loop Continue statement – goto Statement.(6)

FUNCTIONS:Modular Programming – Function Prototypes - Defining and Calling Functions –Function Call Stack and Activation Records - Passing Arguments to Functions – Returning a value from a function- Recursion – Recursion vs. Iteration – Scope and lifetime of variables – Memory layout of a C program - Storage Classes - Auto - Static - Extern and Register Variables.(8)

ARRAYS:Defining Array –Array Initialization - Accessing array elements - Processing arrays - Arrays as function arguments - Multidimensional arrays – Memory address calculation of an array – Row major and column major order - String Handling. (8)

POINTERS: Pointer Variable Definitions and Initializations – Passing Arguments to Functions by address – Pointer Expressions and Pointer Arithmetic - Relationship between Pointers and Arrays - Pointers and multidimensional arrays –Constant Pointer – Pointer to Constant –NULL pointer- dangling pointers - Pointers to functions - passing functions to other functions – Introduction to Stack and Heap Memory - Dynamic Memory Allocation.(10)

STRUCTURES AND UNIONS: Structure Definitions – Initializing Structures – Accessing Structure Members - Processing a structure - typedef- Structures and pointers - Passing structures to functions – Self-Referential Structures- Bit fields - Unions – Enumeration Constants.(8)

FILES:Files and Streams - Operations on Files – Types of Files, Various Read and Write Functions for Sequential-Access and Random-Access Files -Command Line Arguments. (5)

PREPROCESSOR DIRECTIVES#include Preprocessor Directive - #define Preprocessor Directive: Symbolic Constants - #define Preprocessor Directive: Macros - Conditional Compilation (4)

Total L:60

TEXT BOOKS:
1. Paul Deital and Harvey Deital, “C How to Program”, Pearson, 2021.
2.2. Brian W. Kernighan and Dennis Ritchie, “The C Programming Language”, Pearson, 2019.
3. R G Dromey, “How to solve it by computer”, Pearson 2008.

REFERENCES:
1. Herbert Schildt, “C The Complete Reference", McGraw Hill, 2019.
2. Gottfried B, “Programming With C”, McGraw Hill, 2011.

3 0 0 3

COMMUNICATION SKILLS USING SCIENTIFIC TEXTS: Comprehension and critical evaluation of Scientific Essays – Focus on Language Style, Word Formation, Use of Prefixes and Suffixes, Synonyms, Antonyms, Abbreviations and Acronyms and Technical Vocabulary.(6)

FOCUS ON GRAMMAR: Identifying Common Errors In Articles And Prepositions, Common Errors-Misplaced Modifiers-Tenses-Redundancies And Clichés-Practice Exercises In Common Errors. (6)

READING: Reading and Importance-Techniques Of Effective Reading-Improving Comprehension Skills-Techniques For Good Comprehension-Skimming And Scanning-Comprehension-Intensive And Extensive Reading-Practice In Reading Comprehension.(4)

WRITING: Formal Letters-Letter of Complaint, Requisition Letter- Job Application and Resume- Report Writing- Types Of Reports- Business And Technical Reports.(6)

FOCUS ON SOFT SKILLS: Intra & Interpersonal Communication-Interview Techniques-Etiquette-Body Language-Telephone Conversation. (8)

PRACTICALS: Presentations-Group Discussions-Listening Exercises-Mock Interviews. (15)

Total L: 45

TEXT BOOKS:
1. N.P.Sudharshana, C. Savitha “English for Engineers”, Cambridge University Press, 2018.

REFERENCES:
1. Dhanavel, S.P, “English and Soft Skills”, Orient BlackSwan, Hyderabad, 2010.
2. Lina Muhkopadhyay, et al., “English for Jobseekers”, Cambridge University Press, New Delhi, 2013.

0 0 4 2

1.Functions
2.Limits and Continuity of functions
3.Plot 2D and 3D functions
4.Taylor series for functions of single variable
5.Matrix operations
6.Factorization of polynomials
7.Differentiation and integration
8.Finding maxima and minima
9.Evaluation of multiple integrals
10.Solving differential equations

Total P: 60

REFERENCES:
1. Amos Gilat, “MATLAB: An Introduction with Applications”, Wiley, 2016.
2.Eugeniy E. Mikhailov, “Programming with MATLAB for Scientists a Beginner’s Introduction”, CRC press, 2018.
3. 3. Stephen J. Chapman, “MATLAB Programming for Engineers”, Cengage Learning, 2016.

0 0 4 2

1. Simple programs to understand the concepts of data types.
2. Familiarizing conditional, control and repetition statements
3. Usage of single and double dimensional arrays including storage operation
4. Implementation of functions, recursive functions
5. Defining and handling structures, array of structures and union
6. Implementation of pointers, operation on pointers dynamic storage allocation
7. Creating and processing data files.

Total P: 60

0 0 2 1

DIGITAL ELECTRONICS LABORATORY
1. Study of basic logic gates and realization of logic gates using universal gates.
2. Multiplexer and demultiplexer.
3. Half and full adder / subtractor.
4. Encoder and decoder.
5. Binary decade counter.
6. BCD to seven segment decoder.
7. Study of D/A converter.
8. Crystal Oscillator using logic gates

Total P: 30

3 2 0 4

MATHEMATICAL LOGIC: Propositional Logic, Laws of Logic, System Specifications, Propositional Equivalences, Normal forms. Propositional Calculus, Predicates and Quantifiers, Logical Equivalences Involving Quantifiers, Rules of inference, Validity of arguments. (10+7)

PROOF TECHNIQUES: Methods of proving theorems – Direct proofs, Indirect proofs – Mathematical induction – Strong mathematical induction and Well ordering principle. (6+4)

RELATIONS AND FUNCTIONS: Binary relations, Properties and Representation of Relations – Closures of Relations – Composition of Relations – Equivalence Relations– Partial Orderings – Partitions and Covering of Sets, Hasse Diagrams, n-ary Relations and their Applications. Functions - Injective, Subjective, Bijective, Composition, and Inverse. (10+6)

COMBINATORICS: Basics of counting – Principle of inclusion and exclusion, Pigeonhole principle. Permutations and Combinations without repetition, Permutations and Combinations with repetition, Permutations with Indistinguishable Objects, Distributing Objects into Boxes, Generating permutations and combinations in lexicographic order. (8+4)

RECURRENCE RELATIONS: Recurrence Relation Models- Solving linear homogeneous recurrence relations with constant coefficients, Solving linear non-homogeneous recurrence relations by the method of characteristic roots. (5+4)

FORMAL LANGUAGES: Four classes of grammars (Phrase Structure, Context sensitive, Context Free, Regular) –Language of grammar, Regular Expressions, Context free languages: generation trees - ambiguity. (3+3)

FINITE AUTOMATA: Deterministic Finite State Automata (DFA) - Non-deterministic Finite State Automata (NDFA) - Conversion of NDFA to DFA (3+2)

Total L:45+T:30=75

TEXT BOOKS:
1. Kenneth H. Rosen, “Discrete Mathematics and its Application”, McGraw Hill, 2021.
2. Tremblay J. P. and Manohar R., “Discrete Mathematical structures with application to Computer Science”, McGraw Hill, 2017.

REFERENCES:
1. Bernard Kolman, Robert C Busby and Sharon Ross, "Discrete Mathematical Structures", Pearson, 2015.
2. John E Hopcroft, Rajeev Motwani and Jeffrey D Ullman, "Introduction to Automata Theory, Languages and Computation", Pearson, 2014.
3. Ralph P Grimaldi, “Discrete and Combinatorial Mathematics – An Applied Introduction”, Pearson, 2019.

3 2 0 4

ALGEBRAIC STRUCTURES: Groups - Definition and Example, Properties of Groups, Permutation Groups, Symmetric Groups, Cyclic Groups. (8+8)

SUBGROUPS AND NORMAL SUBGROUPS: Subgroups – Definition, Cosets and Lagrange’s theorem, Homomorphism, Isomorphism, Automorphism – Cayley’s theorem – Normal subgroups – Factor group – Fundamental theorem of group homomorphism (8+5)

GROUPS AND CODING: Coding of Binary information and Error detection – Group codes – Decoding and Error correction. (8+3)

RINGS: Definition and Properties – Subrings, Ring of Quaternions, Integral domain - Homomorphism – Ideals and Quotient Rings – Euclidean ring - Unique factorization theorem, Domain of Gaussian Integers. Polynomials Rings – Properties, Division Algorithm, Factorization of Polynomials – Primitive polynomials. (10+8)

FIELDS: Definition – subfields - Finite fields – structure of Finite field, GF (2n). (11+6)

Total L : 45+ T:30=75

TEXT BOOKS:
1. Herstein I. N., “Topics in Algebra”, John Wiley , 2022.
2. Joseph A. Gallian, "Contemporary Abstract Algebra" Chapman and Hall, 2021.
3. 3. Humphreys, J. F and Prest, M. Y. "Numbers, Groups and Codes" Cambridge University Press, 2018.

REFERENCES:
1. Ron M. Roth, “Introduction to Coding Theory”, Cambridge University Press, 2020.
2. Ralph P. Grimaldi and Ramana B. V., “Discrete and Combinatorial Mathematics: An Applied Introduction”, Pearson 2014.

3 0 0 3

INTRODUCTION: Primitive Data Structures - Abstract Data Types - Analysis of algorithms – Best and worst case time complexities – Asymptotic notation – Growth of functions. (4)

ARRAYS:Operations and Implementation - Linear search, non-recursive binary search – Sparse matrices – Storage – Basic sparse matrix operations. (4)

STACKS:Primitive operations - Sequential implementation - Applications: Expression processing - Infix to Postfix Conversion - Evaluation of Postfix Expression - Parentheses matching - Recursive functions. (6)

QUEUES: Primitive operations - Sequential implementation – Linear queue- Circular queue - Priority queues – Double ended queues– Applications: CPU scheduling. (4)

LISTS: Primitive operations - Singly linked lists, Doubly linked lists, Circular lists, Multiply linked lists - Application: Addition of Polynomials – Linked Stacks - Linked queues. (7)

TREES: Terminologies – Binary tree -Sequential and linked representation -Traversals -Expression trees (5)

GRAPHS: Terminologies– Representations using Adjacency matrix, adjacency list - Graph Traversal Algorithms: Breadth first and Depth first Search - Time complexity Analysis.

HASH TABLES: Hash functions – Collision handling techniques - Separate chaining, Linear probing, Quadratic probing - Analysis. (4)

SORTING:Insertion sort, Selection sort, Bubble sort, Merge Sort, Radix Sort -Time complexity analysis (7)

Total L: 45

TEXT BOOKS:
1. YedidyahLangsam, Moshe J Augenstein, Aaron M Tenenbaum, “Data structures using C and C++”, Prentice Hall, 2016.
2. SartajSahni, “Data Structures, Algorithms and Applications in C++”, Silicon Press, 2013.
3. Michael T. Goodrich, Roberto Tamassia, David Mount, “Data Structures and Algorithms in C++”, John Wiley, 2016.

REFERENCES:
1. Thomas H Cormen, Charles E Leiserson, Ronald L Rivest, Clifford Stein, “Introduction to Algorithms”, MIT Press, 2022.
2. Mark Allen Weiss, “Data Structures and Algorithm Analysis in C”, Pearson, 2017.
3. Robert L Kruse, Bruce P Leung, Clovis L Tondo, “Data Structures and Program Design in C”, Pearson, 2013.
4. Nell Dale, Chip Weems, Tim Richards, “C++ Plus Data Structures”, Jones & Bartlett, 2017.
5. Alfred V. Aho, John E Hopcraft, Jeffrey D. Ullman,”Data structures and Algorithms”, Pearson, 2011.

3 0 0 3

PRINCIPLES OF OBJECT ORIENTED PROGRAMMING: Software crisis - Software Evolution - Procedure Oriented Programming - Object Oriented Programming Paradigm - Basic Concepts and Benefits of OOP - Object Oriented Programming Language - Application of OOP - Structure of C++ - Tokens, Expressions and Control Structures - Operators in C++ - Manipulators. (6)

FUNCTIONS IN C++: Function Prototyping - Call by Reference - Return by reference - Inline functions - Default, Const Arguments - Function - Overloading - Classes and Objects - Member functions - Nesting of Member functions - Private member functions - Memory allocation for Objects - Static data members - Static Member Functions - Arrays of Objects - Objects as Function Arguments - Friend Functions - Returning Objects - Const Member functions - Pointers to Members. (10)

CONSTRUCTORS: Parameterized Constructors - Multiple Constructors in a class - Constructors with Default Arguments - Dynamic Initialization of Objects - Copy and Dynamic Constructors – Destructors overloading . (5)

OPERATOR OVERLOADING: Overloading Unary and Binary Operators - Overloading Binary Operators using Friend functions – Operator Type conversion. (4)

INHERITANCE: Defining Derived Classes - Single Inheritance - Making a Private Member Inheritable – Multiple Inheritance- Multilevel Inheritance - Hierarchical Inheritance - Hybrid Inheritance - Virtual Base Classes - Abstract Classes - Constructors in Derived Classes - Member Classes - Nesting of Classes – Composition – Aggregation (9)

POLYMORPHISM: Basics of polymorphism – Types of polymorphism - Compile and Run Time Polymorphism - Virtual function – Object Slicing – Virtual Destructor – Dynamic binding (4)

TEMPLATES & EXCEPTION HANDLING: Introduction to Templates, Generic Functions and Generic Classes – Exception Handling – Examples. (3)

STREAMS: String I/O -Character I/O - Object I/O - I/O with multiple Objects - File pointers - Disk I/O with member function (4)

Total L: 45

TEXT BOOKS:
1. BjarneStroustrup, “The C++ Programming Language”, Pearson Education, 2014.
2. Stanley B. Lippman, JoseeLajoie and Barbara E. Moo, “The C++ Primer”, Addison Wesley,2013.

REFERENCES:
1. Harvey M Deitel,Paul J Deitel, “C++ How to Program”, Prentice Hall, 2014.
2. Herbert Schildt, “C++ - The Complete Reference", Tata McGraw Hill, 2012.

4 0 0 4

PROBABILITY BASIC CONCEPTS: Introduction - Sample space and events - Axiomatic approach to probability – Basic theorems. Conditional Probability - Law of multiplication - Law of total probability and Bayes’ Theorem - Independence. (10)

RANDOM VARIABLES: Discrete and continuous random variables - probability mass function and density function - distribution function - Expectation and variance. (8)

THEORETICAL DISTRIBUTIONS: Discrete: Binomial, Poisson and Geometric - Continuous: Uniform, Normal, Exponential, Weibull, Erlang and Gamma.   (10)

BIVARIATE DISTRIBUTIONS: Joint probability distributions - Marginal and conditional distributions - Statistical independence - Conditional expectation – Transformation of two random variables. (10)

LIMIT THEOREMS: Moments and moment generating functions- Sums of independent random variables - Limit theorems: Markov and Chebyshev inequalities, Law of Large numbers, Central Limit Theorem. (10)

RELIABILITY: Introduction - Structure Functions - Reliability of Systems of Independent Components - System Life as a Function of Component Lives - Expected System Lifetime. (12)

Total: L:60

TEXT BOOKS:
1. SaeedGhahramani, “Fundamentals of Probability with Stochastic Processes”, Pearson Education, 2018.
2. Sheldon M.Ross, “Introduction to Probability Models”, Academic Press, 2014.
3. Ronald E. Walpole, Raymond H. Meyers and Sharon L. Meyers, “Probability and Statistics for Engineers and Scientists”, Pearson Education, 2014.

REFERENCES:
1. Anthony J. Hayter, “Probability and Statistics for Engineers and Scientists“, Cengage Learning, 2013.
2. Jay L Devore, “Probability and Statistics for Engineering and Sciences”, Cengage Learning, 2020.
3. Richard A. Johnson, Irwin Miller and John Freund, “Probability and Statistics for Engineers”, Pearson Education, 2014.
4. K. S. Trivedi, “Probability and Statistics with Queuing, Reliability and Computer Science Applications”,John Wiley , 2016.

0 0 4 2

Exercises pertaining to the following outlines are to be experimented using C++:

1. Arithmetic operations using array of objects and dynamic data members.
2. Creation of a class having read-only member function and processing the objects of that class.
3. Creation of a class which keeps track of the member of its instances. Usage of static data member, constructor and destructor to maintain updated information about active objects.
4. Illustration of a data structure using dynamic objects.
5. Usage of static member to count the number of instances of a class.
6. Illustration for the need of default arguments.
7. Usage of a function to perform the same operation on more than one data type.
8. Creation of a class with generic data member.
9. Overloading the operators to do arithmetic operations on objects.
10. Acquisition of the features of an existing class and creation of a new class with added features in it.
11. Implementation of run time polymorphism.
12. Overloading stream operators and creation of user manipulators.
13. Implementation of derived class which has direct access to both its own and public members of the base class.
14. Implementation of Streams to store and maintain Library system, with the features of Book Issue and Book Return.

Total: P:60

23XD27 DATA STRUCTURES LAB

0 0 4 2

Implementation of the following problems:

1. Time complexity based problems on arrays, matrices and strings
2. Sparse matrix operations using arrays.
3. Stacks and queues using arrays.
4. Singly linked, doubly linked and Circular linked lists.
5. Linked Stacks, Linked queues and priority queues
6. Binary trees.
7. Graph traversal algorithms.
8. Dictionary using Hash Tables
9. Sorting algorithms.

Total: P:60

0 0 4 2

INTRODUCTION: Python Interpreter – Program execution – Interactive prompt – IDLE User Interface.

TYPES AND OPERATIONS: Python object types – Numeric types – Dynamic typing – String fundamentals – Lists – Dictionaries - Tuples – Type objects.

STATEMENTS AND SYNTAX: Python statements - Assignments – Expressions – if Tests – while Loops - for Loops – Iterations – Comprehensions.

FUNCTIONS AND GENERATORS:Function basics – Scopes – Arguments – Recursive functions – Anonymous functions – lambda – Generator functions -

MODULES AND PACKAGES: Python program structure – Module Imports – Standard library modules – Packages – Namespaces,pip.

STANDARD PACKAGES: NumPy – Matplotlib – SciPy – SymPy – Pandas.

FILES: Opening files – Reading and writing files – Text files – Binary files.

CASE STUDIES: Real-time applications using libraries like Tkinter, Django, Urllib, BeautifulSoup, Statsmodels. Seaborn, etc.

Implementation of the following problems using Python programming:
1. Testing basic coding skills in Python using data types, control statements and iteration.
2. Implementing Python data structures like lists, tuples, dictionaries, and sets.
3. General programming concepts such as functions, strings, regular expressions, reading / writing files and exceptions.
4. Packaging programs into reusable libraries.
5. Usage of mathematical Libraries like Sympy
6. Implement object-oriented concepts.
7. Creating and processing data files using Pandas
8. Plotting Probability distributions
9. Use libraries for numerical programming and data visualization – Numpy and Matplotlib etc

REFERENCES:
1. Tony Gaddis, “Starting out with Python”, Pearson, 2021.
2. Christian Hill, “Learning Scientific Programming with Python”, Cambridge University Press, 2020.
3. John M. Stewart, “Python for Scientists”, Cambridge University Press, 2017.
4. Kent D. Lee, “Python Programming Fundamentals”, Springer, 2014.
5. Allen Downey, “Python for Software Design”, Cambridge University Press, 2009.

Total: P:60

3 0 0 3

DESCRIPTIVE STATISTICS: Frequency distribution – Bar graphs and Pie charts – Histogram- Ogive – Simpson’s paradox – Measures of Location – Measures of Variability – Measures of distribution shape, relative location and detecting outliers – Exploratory Data analysis, Stem-and-leaf display – Measures of Association between two variables. (15)

STATISTICAL INFERENCE: Sampling distribution - Estimation: Point estimation, interval estimation - Criteria of a good estimator –Interval estimation of mean, proportion, and variance (single sample and two samples) - Maximum likelihood estimator. Incorporating priors with Bayesian inference,exponential family, prior and posterior distributions, conjugate prior distributions,Bayesian inference for Normal distributions;Hypothesis Testing: General concepts - Errors in Hypothesis testing - One-and two-tailed tests - Tests concerning mean, proportion, and variance - Tests for Goodness of fit and independence of attributes. (15)

CORRELATION AND REGRESSION: : Introduction - Estimation using the regression line - Correlation analysis -Limitations, errors, and caveats of using regression and correlation analyses - Multiple regression and correlation analysis - Inferences about population parameters – Modeling techniques. (10)

ANALYSIS OF VARIANCE: Introduction to design of experiments, Analysis of variance - Completely Randomized Design and Randomized Block Design. (5)

Total L: 45

TEXT BOOKS:
1. Jay L. Devore, “Probability and Statistics for Engineering and Sciences”, Cengage Learning, 2020.
2. David Forsyth, ‘Probability and Statistics for Computer Science’, Springer; 2018
3. Michael J. Evans, Jeffrey S. Rosenthal, ‘Probability and Statistics - The Science of Uncertainty’. W H Freeman , 2010

REFERENCES:
1. Anderson, Sweeney and Williams “Statistics for business and economics”, Cengage Learning, 2014.
2. Douglas C Montgomery and George C Runges, “Applied Statistics and Probability for Engineers”, John Wiley, 2014.
3. Roy D.Yates and David J. Goodman, “Probability and Stochastic Processes – A friendly Introduction for Electrical and Computer Engineers”, John Wiley, 2014.
4. Trivedi K.S., “Probability and Statistics with Reliability, Queueing and Computer Science Applications”, John Wiley , 2016

3 2 0 4

SYSTEM OF LINEAR EQUATIONS:System of linear equations, Gauss - elimination, Gauss - seidel method - Application of Linear systems. (7+4)

VECTOR SPACES: Vector spaces and subspaces - Span, Linear independence and dependence - Basis and dimension - Row space, Column space, and Null space - Rank and nullity - Change of basis - Similarity - Isomorphism. (10+7)

LINEAR TRANSFORMATION: Introduction to linear transformations - General Linear Transformations - Kernel and range -Matrices of general linear transformation - Geometry of linear operators. (8+5)

EIGEN VALUES AND EIGEN VECTORS:: Introduction to Eigenvalues Eigenvectors, Complex Eigenvalues - Diagonalization - Orthogonal diagonalization - Positive definite matrices - Quadratic forms - Quadric surfaces - Singular value decomposition - Applications to differential equations, dynamical systems. (10+7)

INNER PRODUCT SAPCES: Inner products, Length and Angle in inner product spaces - Orthonormal bases, Gram Schmidt process - Orthogonal matrices - QR decomposition - Best Approximation and Least-squares (10+7)

Total L : 45+T:30=75

TEXT BOOKS:
1. David C. Lay, “Linear Algebra and its Applications”, Pearson, 2020.
2. Kenneth Holfman and Ray Kunze, “Linear Algebra”, Pearson, 2022.

REFERENCES:
1. Gilbert Strang, “Linear Algebra and its Applications”, Thomson Learning, 2022.
2. Steven J. Leon, “Linear Algebra with Applications”, Prentice Hall, 2019.
3. Yousef Saad, “Numerical methods for Large Eigenvalue Problems”,Manchester University Press, 2017.
4. Howard Anton, Chris Rorres, “Elementary Linear Algebra”, Wiley, 2020.

4 0 0 4

INTRODUCTION: Graphs and digraphs, graph models, matrix representations, Hand-shaking lemma, degree sequence, Havel-Hakimi theorem, subgraphs, walk, trail, path, connectedness, distance, radius, diameter, Common families of graphs, isomorphic graphs. Trees - spanning trees, characterizations, Matrix tree theorem. (10)

CONNECTIVITY: Vertex and edge cuts, blocks, Vertex and edge connectivity, relationship between vertex and edge connectivity. Whitney’s theorem, 2-connected graphs, Menger’s theorem - Harary’s construction of optimal k-connected graphs. Connectedness in digraphs. (12)  

MATCHING, VERTEX-COLORING AND DOMINATION:Maximum Matching - Perfect matching - augmenting path - Bipartite matching - Hall’s theorem - Job assignments. Vertex cover - minimum vertex cover - Independent set - Konig’s theorem. Dominating set - total, independent, bipartite, connected, distance dominations, domination number and bounds - Relationship between dominating set and vertex cover. (14)  

EULERIAN AND HAMILTONIAN GRAPHS: Eulerian trails, characterizations, computing Eulerian cycles. Hamiltonian cycle, Dirac’s and Ore’s conditions, Gray codes. (10)

VERTEX COLOURING AND PLANARITY: Proper vertex-coloring, chromatic number, bounds, Brook’s and Welsh-Powell theorems. Sequential and Largest degree first vertex coloring algorithms. Planarity and non-planarity, Euler’s formula, Kuratowski’s theorem (statement). Vertex coloring in planar graphs. (14)

Total  L: 60

TEXT BOOKS:
1. Bondy J.A., Murty U.S.R., “Graph Theory”, Springer, 2013.
2. Douglas B. West, “Graph Theory”, Prentice Hall, 2018.
3. Balakrishnan R and Ranganathan, K, “A Textbook of Graph Theory”, Springer 2019.

REFERENCES:
1. Jonathan Gross, Jay Yellen, Mark Anderson, “Graph Theory and its Applications”, Chapman and Hall/CRC Press, 2018.
2. Thulasiraman K, Swamy M N S, “Graphs: Theory and Algorithms”, John Wiley, 2014.
3. Teresa W. Haynes, Stephen T. Hedetniemi, Michael A. Henning, “Topics in Domination in Graphs”, Springer, 2020.

3 0 0 3

INTRODUCTION:Asymptotic analysis of algorithms – Average case time complexity. (4)

BINARY SEARCH TREES: Searching – Insertion and deletion of elements - Analysis (4)

AVL TREES: Height – Searching – insertion and deletion of elements- AVL rotations – analysis. (5)

SPLAY TREES: Searching, Insertion and Deletion Operations – Amortized time complexity - amortized analysis on Splay trees (6)

MULTIWAY SEARCH TREES: Indexed Sequential Search - m-way search trees – B-Tree – Searching, Insertion and deletion - Analysis - B+ trees: Operations and Analysis - Tries - Suffix trees (10)

MULTIDIMENSIONALSEARCH TREES:Range search - k-d trees - quad trees (4)

HEAPS: Min Heap - Max Heap - Operations - Heap Sort - Binomial heap - Analysis (8)

DATA STRUCTURES FOR DISJOINT SETS: : Disjoint set operations-linked list representation of disjoint sets, disjoint set forests, tree representation, union by rank, find by path compression. (6)

Total L: 45

TEXT BOOKS:
1. Thomas H. Cormen, Charles E.Leiserson and Ronald L. Rivest, “Introduction to Algorithms” , MIT Press, 2022.
2. Mark Allen Weiss, “Data Structures and Algorithm Analysis in C++”, Addison-Wesley, 2014.
REFERENCES:
1. Robert L. Kruse and Clovis L.Tondo, “Data Structures and Program design in C”, Pearson 2013.
1. Michael T. Goodrich, Roberto Tamassia, David Mount, “Data Structures and Algorithms in C++”, John Wiley, 2016.
3. Sahni Sartaj, "Data Structures, Algorithms and Applications in C++", Silicon Press, 2013.

3 2 0 4

INTRODUCTION: Basic principles - Functional components of a computer system- CPU, Storage, I/O, Multimedia devices - Workstations, Servers -Interaction among functional components -Bus organization -Data representation- Integer and floating point representation – IEEE representation (6)

BASIC PROCESSING UNIT:Fundamental Concepts-- Computer registers -Register transfer language -Generation and Execution of machine code- Hardware Components- Fetch and Execution Steps - register, memory and I/O operations (6)

MEMORY SYSTEM :: Basic Concepts- Internal Organization of Memory - Semiconductor RAM Memories - Static and Dynamic RAMs - Read-only Memories - Flash Memory - Direct Memory Access - Cache Memories - Performance Considerations - Virtual Memory – Segmentation. (8)

PIPELINING -:Basic Concept of pipelining - Pipeline Organization - Pipelining Issues - Data Dependencies - Operand Forwarding - Handling Data Dependencies in Software - Memory Delays- Branch Delays - Branch Prediction -Resource Limitations -Performance Evaluation. (7)

MICROPROCESSORS: Architecture of 8086 – Memory address space and Data organization - Generating memory address - Addressing modes – Instruction format -Instruction Set Architecture (ISA) (6)

PROGRAMMING: Arithmetic and Logic Instructions – Control flow instructions and program structures - Array processing- String processing - Assembler directives -Procedures and Macros. (8)

INTERRUPTS: : Introduction - Types of interrupts – Priorities of interrupt – Interrupt Instructions in 8086 - Implementing Interrupt scheme (4)

TUTORIAL:
1. Arithmetic operations of binary numbers using both one’s complement and two’s complement.
2. Implement parity bit generation for a n-bit binary data.
3. Hands on the emu8086 Tool.
4. Data Transfer and Data Manipulation Instructions.
5. Implementation of Control Structures
6. Array processing
7. String processing
8. Procedures and macros
9. Interrupts

Total L: 45 + T:30 = 75

TEXT BOOKS:
1. Computer Architecture and Organization Designing for Performance, William Stallings, Pearson series, 2022.
2. Computer Organization and Design :The Hardware/Software Interface, David A. Patterson and John L. Hennessy, Morgan Kaufmann, 2017
3. Barry B. Brey, The Intel Microprocessors - 8086/88, and 80186, 80286, 80386, and 80486, Pearson, 2017.
4. Walter A. Triebel, Avtar Singh, “8088 and 8086 Microprocessors Programming”, Pearson, 2017.

0 0 4 2

Implementation of the following problems using Statistical Packages:
1. Classification and tabulation of data and Graphical and diagrammatic presentation of data.
2. Perform calculations that measure the central tendency and dispersion of data and Implementation of measures of Skewness, moments and kurtosis.
3. Setup priors, likelihood from data, and compute the posterior distribution.
4. Conjugate priors for exponential family of distributions.
5. Determination of point estimate: MLE,MAP, Bayesian Estimates.
6. Determination of interval estimates
7. Testing of hypothesis – large samples tests, small samples tests
8. Solving linear regression, polynomial regression and non-linear regression based problems and solving multiple regression and correlation analysis based problems.

Total P:60

0 0 4 2

Implementation of the following problems:
1. Applications of binary search tree and its operations.
2. AVL trees.
3. Splay trees.
4. B-trees
5. Heap and its operation.
6. Disjoint set.

Total P:60

0 0 4 2

SOLUTION OF ALGEBRAIC EQUATIONS: Newton Raphson method, Modified Newton Raphson method, Method of false position, Graffe’s root squaring method, Bairstow’s method.

SOLUTION OF ALGEBRAICSIMULTANEOUS EQUATIONS: Gauss – Jordan elimination, Cholesky method, Crout’s method, Gauss – Jacobi method, Gauss – Seidel method. Matrix Inverse by Gauss – Jordan method.

EIGENVALUES AND EIGENVECTORS: Power method for finding dominant eigenvalue and inverse power method for finding smallest eigenvalue, Jacobi method for symmetric matrices.

INTERPOLATION AND CURVE FITTING: Finite difference operators-Interpolating Polynomials, Divided Difference, Spline Curves, Bezier Curves and B-Spline Curves. Solution of linear second order difference equations with constant coefficients.

PARTIAL DIFFERENTIAL EQUATIONS: Classification of partial differential equations of second order. Liebmann’s method for Laplace equation and Poisson equation, Explicitmethod and Crank – Nicolson method for parabolic equations. Explicit method for hyperbolic equations.

LABORATORY COMPONENT:
1. Solution of algebraic and transcendental equations
2. solving linear system of equations by direct method and iterative method
3. Computing Eigen value and Eigen vectors
4. Interpolation with unequal intervals and equal intervals
5. Numerical Differentiation and Integration
6. Taylor’s series method, Euler’s method, Modified Euler’s method Fourth order Runge Kutta method for solving first order differential equations
7. Numerical solutions of Solution of one dimensional heat equation by explicit and implicit methods – One dimensional wave equation and two dimensional Laplace and Poisson equation

Total P:60

REFERENCES:
1. Steven C. Chapra and Raymond P. Canale, “Numerical Methods for Engineers with Software and Programming Applications”, McGraw Hill, 2011.
2. CurtisF. Gerald,andPatrickO. Wheatley, “Applied Numerical Analysis” Pearson,2011.
3. Richard L. Burden and J. Douglas Faires,”Numerical Analysis”, Cengage Learning, 2011.
4. Yousef Saad. “Numerical methods for large eigenvalue problems”, University Press, 2011.

4 0 0 4

Constrained optimization - Linear Programming: Linear programming model - Graphical solution for two dimensional problems – Fundamental Theorem of Linear Programming, Simplex, Two phase Simplex, Revised Simplex methods. Dual problem, Dual Simplex algorithm, sensitivity analysis. (15)

Transportation and Assignment Models: Transportation problem, solution by Modified Distribution method. Assignment problem, solution by Hungarian method. (8)

Unconstrained optimization -Nonlinear programming: Necessary, sufficient conditions, zeroth-order, first-order and second-order methods. Direct search methods – Univariate method, pattern search method. Descent method – Steepest descent (Cauchy) method, Fletcher-Reeves Conjugate gradient method. (12)

Constrained optimization - Nonlinear Programming: Equality constraints - Method of Lagrange multipliers and interpretation. Inequality constraints – Karush-Kuhn-Tucker (KKT) conditions. (10)

Convex Optimization: Convex function, epigraph, polyhedral function, convex and affine hulls.Convex optimization problems – First-order and Second-order conditions, sufficiency of KKT conditions. (15)

Total L:60

TEXT BOOK:
1. David G. Luenberger, Yinyu Ye, “Linear and Nonlinear Programming”, Springer, 2021.
2. Hamdy A Taha, “Operations Research – An Introduction”, Pearson, 2017.
3. Stephen Boyd, LievenVandenberghe, “Convex Optimization”, Cambridge University Press, 2016.
REFERENCES:
1. Hillier F and Liberman G J, “Introduction to Operations Research”, McGraw Hill,2014.
2. Igor Griva, Stephen G. Nash, ArielaSofer, “Linear and Nonlinear Optimization”, SIAM, 2011.
3. John M. Lewis, S. Lakshmivarahan, Sudarshan K Dhall, “Dynamic Data Assimilation: A Least Squares Approach”, Cambridge University Press, 2006.
4. Kambo N S, “Mathematical Programming Techniques”, East-West Press, 2012.
5. Rao Singiresu S, Engineering Optimization: Theory and Practice, Wiley, 2019.

3 0 0 3

BASIC CONCEPTS: Introduction to databases – Characteristics of database approach- Conventional file processing –– Advantages of using DBMS – Database System Concept and Architecture: Data Models – Instances and Schema – Three Schema Architecture - Data Independence – Components of a DBMS. (5)

CONCEPTUAL DATA MODELLING: ER DATA MODEL: Entities, Attributes, Relationships – Role and Structural constraints – Weak and Strong entity types – Entity Relationship diagrams – Generalization – Aggregation– Applications – Introduction to Network data model and Hierarchical data model . RELATIONAL MODEL: Basic concepts – Constraints – Mapping ER model into Relational model (12)

RELATIONAL QUERIES: Relational Algebra - Tuple relational calculus - Structured Query Language (SQL): SQL Commands for CRUD operations – Functions in SQL – Aggregation – Categorization – Views in SQL –– PL/SQL Basics – Procedures – Functions – Triggers. (7)

RELATIONAL DATABASE DESIGN: Anomalies in a database – Functional dependencies – Axioms – Normal forms based on primary keys – Second Normal form, Third Normal form, Boyce – Codd Normal form – Examples – Multi-valued dependencies – Fourth Normal form – Join Dependency - Fifth Normal Form - Physical database design and tuning (8)

FILE ORGANIZATION: Storage device characteristics – Constituents of a file – Operations on file – Sequential files – Index sequential files – Direct files – Primary and Secondary Key Retrieval – Types of indexes - Indexing using Tree Structures – SQL Query Processing (6)

TRANSACTION PROCESSING AND CONCURRENCY CONTROL: Transactions, Locking techniques, Concurrent access, Deadlock handling. (4)

DATABASE SECURITY, INTEGRITY CONTROL: Security and Integrity threats – Defense mechanisms – Discretionary Access Control and Mandatory Access Control. (3)

Total L: 45

TEXT BOOKS:
1. Elmasri R and Navathe SB, “Fundamentals of Database Systems”, Pearson Education, 2017.
2. Silberschatz A, Korth H and Sudarshan S, “Database System Concepts”, McGraw Hill, 2019.

REFERENCES:
1. Hector Garcia Molina, Jeffrey D. Ullman, Jennifer Widom, “Database Systems: The Complete Book” , Pearson Education, 2011.
2. Raghu Ramakrishnan and Johannes Gehrke, “Database Management System”, McGraw Hill, 2018.

3 0 0 3

DATA WRANGLING: DataIngest,Data Cleaning - Exploratory data analysis - Univariate data – Bivariate data, Multivariate data (5)

LINEAR REGRESSION: Coefficient of determination, Significance test, Residual analysis, Confidence and Prediction intervals. (5)

MULTIPLE LINEAR REGRESSION: Coefficient of determination, Interpretation of regression coefficients, Categorical variables, heteroscedasticity, Multi-co linearity outliers, Auto regression and Transformation of variables, Regression, Model Building. (10)

LOGISTIC AND MULTINOMIAL REGRESSION: Logistic function, Estimation of probability using Logistic regression, Variance, Wald Test, Hosmer Lemshow Test, Classification Table, Gini Co-efficient. (5)

DECISION TREES: introduction, CHI-Square Automatic Interaction Detectors (CHAID), Classification and Regression Tree (CART), Analysis of Unstructured data (5)

FORECASTING: Moving average, Exponential Smoothing, Casual Models. (5)

TIME SERIES ANALYSIS: Moving Average Models, ARMA, ARIMA models , Multivariate Models (5)

CASE STUDIES : Application of predictive analytics in retail, direct marketing, health care, financial services, insurance, supply chain, Social media analytics– Customer Analytics - Risk Analytics - Analytics for Retail and Ecommerce, etc- Working with data from different sources: spreadsheets, databases, and the cloud -Model Development- Model Validation (5)

Total L: 45

TEXT BOOKS:
1. Daniel T. Larose, Chantal D. Larose “Data Mining and Predictive Analytics”, Wiley,2015
2. Douglas C. Montgomery, Cheryl L. Jennings, Murat Kulachi, “Introduction to Time Series Analysis and Forecasting” ,Wiley,2015
3. Max Kuhn, Kjell Johnson, “Applied Predictive Modeling”, Springer, 2014.

REFERENCES:
1. Richard A. Johnson, Irwin Miller and John Freund, “Probability and Statistics for Engineers”, Pearson Education, 2014.
2. Ronald E. Walpole, Raymond H. Meyers, Sharon L. Meyers, “Probability and Statistics for Engineers and Scientists”, Pearson Education, 2014.
3. Thomas W.Miller, “Modeling Techniques in Predictive Analytics with Python and R: A guide to Data Science”, Pearson Education, 2014.
4. DursunDelen, Predictive Analytics: Data Mining, Machine Learning and Data Science for Practitioners, O'Reilly,2nd Edition, 2020.

4 0 0 4

INTRODUCTION: Abstract view of an operating system - Operating Systems Objectives and Functions – Evolution of Operating Systems - Dual-mode operation - System calls- Structure of Operating System. (3)

PROCESS DESCRIPTION AND CONTROL:Process concepts - Process Creation – Process Termination - Process states - Process Description – Process Control - shell, boot and init processes in UNIX (5)

PROCESS AND THREADS: Relationship between process and threads – Thread States – Thread Synchronization – Types of Thread – Multithreading model (6)

PROCESS SCHEDULING: Scheduling basics - CPU-I/O interleaving- (non-)preemption - context switching – Saving the context of a process - CPU Scheduler – Types of Scheduling – Scheduling Criteria - Scheduling Algorithms – Algorithm evaluation – Real-time scheduling –Scheduling parameters in UNIX. (7)

PROCESS COMMUNICATION AND SYNCHRONIZATION: PROCESS COMMUNICATION AND SYNCHRONIZATION: Inter-Process Communication (IPC) - Concurrent Process – Principles of Concurrency – Race Condition - Mutual Exclusion – Critical section problems – Software support – Hardware Support – Operating System Support: Semaphore, Monitor – Classical problems of synchronization – Synchronization examples. (8)

DEADLOCK: Principles- Characterization – Methods for handling deadlock - Deadlock prevention, Avoidance, Detection, and recovery. (4)

MEMORY MANAGEMENT: Memory hierarchy –Memory Management requirements - Memory partitioning: Fixed partitioning, dynamic partitioning, Buddy systems – Simple paging – Page table structures – Simple Segmentation – segmentation and paging (8)

VIRTUAL MEMORY MANAGEMENT: Need for Virtual Memory management – Demand Paging –Copy on write – Page Stealer – Page Fault Types - Page Fault handling – Page replacement - Frame allocation- Thrashing - working set model - Swapping (7)

I/O MANAGEMENT AND DISK SCHEDULING: Organization of I/O function – Evolution of I/O function – Types of I/O devices – Logical Structure of I/O functions – I/O Buffering – Disk I/O – Disk Scheduling algorithms – RAID - Disk Cache -– Buffer Cache in UNIX (6)

FILE SYSTEM MANAGEMENT: Files – Access methods - File system architecture – Inode Management in UNIX- Functions of file management –Directory and disk structure -Mounting - File sharing –File system implementation – Directory implementation - File Allocation – Free space management (6)

Total: L: 60

TEXT BOOKS:
1. Silberschatz A, Galvin, PB. and Gagne, G. “Operating System Concepts”, John Wiley & Sons, Inc.,2018.
2. William Stallings, “Operating Systems: Internals and Design Principles”, Pearson Education, 2021.
3. Maurice J Bach, "Design of the UNIX Operating System", Pearson, 2015.

REFERENCES:
1. Andrew S Tanenbaum, Herbert Bos, "Modern Operating System", Pearson, 2022.
2. McHoes, AM and Flynn, I.M. “Understanding Operating Systems”, Cengage Learning, 2016.
3. Dhamdhere D M, “Operating Systems: A Concept-based Approach”, McGraw-Hill, 2015.

4 0 0 4

TRANSFORM METHODS: Concept of Transformation - Integral Transforms – Examples (2)

LAPLACE TRANSFORM: Definition - Transforms of Standard Functions - Transform of unit step function and Dirac delta function – Transform of derivatives and integrals -Transform of periodic functions - Inverse Laplace transform- Convolution Theorem. Method of solving ordinary linear differential equations with constant coefficient and solving integral equations by Laplace Transform technique. (15)

FOURIER SERIES: Even and odd functions, Dirichlet’s conditions, statement of Fourier theorem, Fourier coefficients, change of scale, Half-range sine and cosine series, RMS value, Parseval’s theorem. (13)

FOURIER TRANSFORM : Fourier integrals - Fourier Transform- Transforms of standard functions, Properties. Fourier Sine and Cosine Transform – Properties - Convolution theorem (Statement only). (10)

DISCRETE FOURIER TRANSFORMS:Discrete Convolution – Periodic sequence and circular convolution – Fast Fourier transform - Discrete Fourier Transform – decimation–in-time algorithm – Decimation-in-frequency algorithm – Computation of inverse DFT. (10)

Z-TRANSFORM: Z - Transform of standard functions, inverse Z-transform – properties of Z – Transform – Difference equations – Modeling and solution of difference equations. (10)

Total: L:60

TEXT BOOKS:
1. Ewin Kreyszig, “Advanced Engineering Mathematics”, Wiley India, 2017.
2. Ray Wylie C, Louis C Barret, “Advanced Engineering Mathematics”, McGraw Hill, 2013.

REFERENCES:
1. Michael D. Greenberg, “Advanced Engineering Mathematics”, Pearson Education, 2013.
2. Lokenath Debnath, Dambaru Bhatta, Integral Transforms and their Applications, CRC Press 2015.
3. Dennis G. Zill, Warren S. Wright, “Advanced Engineering Mathematics“, Jones & Bartlett, 2014

0 0 4 2

INTRODUCTION - Understanding data, Data Models, Visualization-the Medium, Representing Data, Exploring Data Visually, and Visualizing with Clarity, Designing for an Audience. The Value of Visualization - Record Information, Support Reasoning, Make a Decision, Data in Context, Find Patterns, Inspire. (3)

DATA MODELS - Taxonomy of Data Types, Nominal, Ordinal & Quantitative, Dimensions & Measures, Data Tables & Transformations, Common Data Formats.

TAXONOMY – Choosing the appropriated chart type, Comparing Categories, Assessing Hierarchies, Showing changes over time, plotting connections, mapping geo-spatial data. Construction & Evaluation, Feedback. (7)

DESIGN STRUCTURES – Hierarchical, Relational, Temporal, Spatial, Spatio-Temporal, Textual Structures. Maps - Choropleth Maps, Cartograms, Flow Maps. Color - Perception of Color, Chromaticity Diagram, Color Spaces, Color Appearance, Designing Colormaps. (8)

GRAPHICAL PERCEPTION – Design Principles, Signal Detection, Magnitude Estimation, Using Multiple Visual Encodings, Pre-Attentive Processing, Gestalt Grouping, Change Blindness. (4)

EXPLORATORY DATA ANALYSIS – Rise of statistics, Data Wrangling, Data Quality. Visual encoding – Mapping Data to Visual Variables, Encoding Effectiveness, Scales & Axes, Aspect Ratio, Regression Lines, Multidimensional Data, Parallel Coordinates, Dimensionality Reduction. (5)

INTERACTION – Execution & Evaluation, Interactive Visualization, Taxonomy of Interactions, Selection, Brushing & Linking, Dynamic Queries. (3)

BIG DATA VISUALIZATION – Visualizations to present and explore big data – visualization of text data and Protein Sequences (3)

ANIMATION – Motion perception, Animated transitions in visualizations, Implementing animations. (4)

CASE STUDIES
Note: Explore software like R, Python, Tableau, D3.j and Google Charts.(15)

Total P: 60 Hrs

TEXT BOOK:
1. Nathan Yau, "Data points: visualization that means something", John Wiley & Sons, 2013.
2. Juuso Koponen, Jonatan Hildén, "Data Visualization Handbook", Aalto University publication, 2019.
3. Andy Kirk, "Data Visualisation: A Handbook for Data Driven Design", Sage Publications, 2019.

REFERENCES:
1. Colin Ware, "Information Visualization: Perception for Design", Elsevier, 2013.
2. Kieran Healy, "Data Visualization: A Practical Introduction", Princeton University Press, 2018
3. Cole Nussbaumer Knaflic,“Storytelling with Data: A Data Visualization Guide for Business Professionals", John Wiley , 2015.
4. Ware C and Kaufman M, “Visual thinking for design”, Morgan Kaufmann Publishers, 2008.

0 0 4 2

1. Creating database structures such as tables, constraints and views using DDL
2. Practicing DML for manipulation of single, multiple tables and Report Generation.
3. Activating access rights and privileges using DCL.
4. Database programming using PL/SQL- Triggers and stored procedures.
5. Working on TCL commands to manage transactions in databases.
6. Establish Database Connectivity - Applications development.

0 0 4 2

TUTORIAL PRACTICES:
1. Practicing UNIX Commands
2. Writing SHELL Scripts
3. Writing programs using UNIX System Calls
4. Process Creation and Execution
5. Thread Creation and Execution
6. Process / Thread Synchronization using semaphore
7. Developing Application using Inter Process communication (using sharedmemory, pipes or message queues)
8. Implementation of Memory Management Schemes
9. Implementation of file allocation technique (Linked, Indexed, Contiguous)

3 0 0 3

INTRODUCTION: Fundamentals of algorithmic problem solving – Methods of specifying an algorithm – Proving the correctness (4)

ANALYSIS OF ALGORITHM: Properties of asymptotic notation - analysis of recursive algorithms: recursion Tree method – Master Theorem. (4)

DIVIDE AND CONQUER: Binary search - Merge Sort - Quick sort - Large Integer multiplication - Strassen’s matrix multiplication - Closest pair. (6)

GREEDY METHOD: Minimum cost spanning tree: Kruskal’s and Prim’s, Topological sorting, Huffman codes - Optimal caching (7)

DYNAMIC PROGRAMMING: Principles of dynamic programming – 0/1 Knapsack problem- Longest common subsequence problem - All pairs shortest problem- Optimal binary search trees- Travelling salesman problem. (9)

STRING MATCHING: The Naïve Method, Rabin – Karp Algorithm, The Knuth- Morris-Pratt Algorithm. (3)

NETWORK FLOW: Flow networks and Flows –Network with multiple sources and sinks - working with flows- Ford - Fulkerson Method- Max- Flow min - cut theorem (5)

NP AND COMPUTATIONAL INTRACTABILITY: Class P - Efficient certification and NP, NP hard and NP complete- Backtracking: n- queens problem, Graph colouring problem, Hamiltonian cycle, Branch and bound: Assignment problem, Travelling salesman problem, 0/1 knapsack problem. Approximation algorithm – Introduction-Traveling salesman problem (7)

Total L: 45

TEXT BOOKS:
1. Thomas H. Cormen, Charles E. Leiserson, and Ronald LRivest, “Introduction to Algorithms”, MIT Press, 2022.
2. Jon Kleinberg and Eve Tardos, “Algorithm Design”, Pearson, 2013.

REFERENCES:
1. Anany Levitin, “Introduction to Design and Analysis of Algorithms”, Pearson, 2014.
2. Michael T. Goodrich and Roberto Tamassia, “Algorithm Design, Foundations, Analysis, and Internet Examples”, Wiley, 2014.
3. Parag H Dave, Himanshu B Dave, “Design and Analysis of Algorithms”, Pearson, 2014.
4. Rolf Niedermeier, “Invitation to Fixed-Parameter Algorithms”, Oxford Univ. Press, 2006.

4 0 0 4

STOCHASTIC PROCESSES: Introduction – Definition- Classification of Stochastic Processes (2)

DISCRETE TIME MARKOV CHAIN: Transition Probability Matrices – Chapman Kolmogorov Equations - Classification of States – Limit Theorems – Branching Processes – Time Reversible Markov chains – Markov Decision Processes - Applications. (12)

CONTINUOUS TIME MARKOV CHAINS: Introduction – Poisson Process - Birth and Death Processes – Kolmogorov Differential Equations – Pure Birth Process - Pure Death Process - Applications. (15)

RENEWAL THEORY: Introduction – Distribution - Renewal Theorems - Residual and Excess Life Times -Alternating Renewal Process - Renewal Reward Processes – Regenerative Processes. (10)

GENERAL QUEUEING MODELS: Single and Multi server Poisson Queues - Single Server Queue with Poisson input and general service M / G/1 – General input and exponential service – G/M/1 Queueing model. (10)

BROWNIAN MOTION: First Passage time distribution – The maximum of a Brownian Motion – The Zeros of Brownian Motion – Brownian Motion with Drift - Geometric Brownian Motion. (11)

Total L:60

TEXT BOOKS:
1. Mark A Pinsky, Samuel Karlin, “An Introduction to Stochastic Modelling”, Academic Press, 2012.
2. Nicolas Privault, “Understanding Markov Chains”, Springer, 2018.
3. Medhi J., “Stochastic Processes”, New Age Science Ltd, 2020.

REFERENCES:
1. SaeedGhahramani, “Fundamentals of Probability with Stochastic Processes”, Pearson, 2019.
2. Sheldon M. Ross, “Introduction to Probability Models”, Academic Press, 2019.
3. Gross.D and Harris C.M, “Fundamentals of Queueing theory”, John Wiley , 2013.

3 2 0 4

INTRODUCTION: Network goals - OSI Reference Model – Network Types and Topologies- Applications (3)

DATA COMMUNICATION: Transmission medium-Impairments-Bandwidth and data rate-.Bit Rate, Baud Rate- Sampling Rate-Types of Multiplexing-Packet Switching (3)

DATA LINK LAYER: Error Detection and Correction - Cyclic Redundancy Check Code -.Hamming Code-Flow Control - ARQ. (6)

LOCAL AREA NETWORKS: Random Access protocols- CSMA CD/CA-Comparative Study of Ethernet , Fast Ethernet and Gigabit Ethernet – Internetworking- LAN -LAN Connections – Repeaters- Hubs - Bridge – Spanning tree-Switches – Routers (6)

IP: TCP/IP Protocol Structure - Internet Protocol – IP addressing-Subnetting-NAT- ARP-DHCP (9)

ROUTING: Distance vector routing - Link state Routing – RIP – OSPF (6)

TRANSPORT LAYER: TCP concepts - Port number -Sockets– Connection control – Congestion Control -UDP (6)

APPLICATIONS: SMTP – HTTP- DNS. (6)

Total L:45

TEXT BOOKS
1. Behrouz A Forouzan, “Data Communications and Networking”, Tata McGraw Hill, 2021.
2. Behrouz A Forouzan, “TCP/ IP Protocol Suite”, Tata McGraw Hill, 2017.
3. Peterson, Larry L., and Bruce S. Davie, “ Computer networks: a systems approach”, Elsevier, 2012.

REFERENCES
1. Kevin Fall R and Richard Stevens W, "TCP/IP Illustrated, Volume 1: The Protocols”, Addison-Wesley, Ann Arbor, 2011.
2. James F. Kurose, Keith Ross, “Computer Networking: A Top-Down Approach”, Addison-Wesley, 2017.
3. Douglas Comer, “Internetworking with TCP/IP”, Prentice Hall, 2013.
4. William Stallings, "Data and Computer Communications”, Pearson, 2014.

3 0 0 3

INTRODUCTION: Basics of Machine learning – Types – Convex set - Convex functions – Convex optimization - Loss functions in machine learning - Gradient descent – variants (5)

SUPERVISED LEARNING: Classification: Linear models – Maximum Likelihood Estimation - Logistic regression – Bayesian Classifier , Maximum A Posteriori estimation (MAP) – Decision theory – Linear Discriminant Analysis –Quadtratic discriminant analysis (QDA) - Support Vector Machines – Linear, Soft margin, (12)

NON-LINEAR MODELS: SVM for linearly non separable data, Kernel functions- Decision trees: Introduction – Purity measures – Entropy, information gain, gain ratio, Gini Index – ID3 –Random forest - K nearest neighbor - Model selection & Evaluation measures (7)

NEURAL NETWORKS: Perceptron - activation functions - Multilayer perceptron - Back propagation – Training Algorithm (5)

UNSUPERVISED LEARNING: Clustering –Types - K-means clustering – Mixture of Gaussians –Spectral clustering - Cluster validity measures – dimensionality reduction- Principal components analysis (PCA) - Independent components analysis (ICA) - Applications : Image segmentation – Image compression – Outlier analysis. (8)

SEMI-SUPERVISED LEARNING: Assumptions – Low density separation – Graph based methods – Generative models – Applications (8)

Total L: 45

TEXT BOOKS:
1. AlpaydinEthem, “Introduction to Machine Learning”, MIT Press, 2020.
2. Christopher M Bishop, “Pattern Recognition and Machine Learning”, Springer, 2016.
3. Richard O Duda, Peter E Hart and David G Stork, “Pattern Classification (Digitized)”, John Wiley, 2012.
4. Oliver Chapelle, Bernhard Scholkopf, Alexander Zian, “Semi-supervised Learning”, MIT Press, 2010.

REFERENCES:
1. Tom M. Mitchell, Machine Learning ,McGraw Hill Education, 2017.
2. ShaiShalev-Shwartz and Shai Ben-David. Understanding Machine Learning. Cambridge University Press, 2017.
3. Trevor Hastie, Robert Tibshirani and Jerome Friedman, “The Elements of Statistical Learning”, Springer, 2013.
4. Kevin Patrick Murphy, “Probabilistic Machine Learning”, MIT Press, 2022.

0 0 4 2

Implement the following

1. Problem Solving using different algorithmic techniques
2. Strassen’s Matrix multiplication
3. Problem using closest pair algorithm
4. Prim’s minimum cost spanning tree
5. Kruskal’s minimum cost spanning tree using min heap data structure, union and find operation
6. Problem related to topological sorting, optimal caching
7. Application of all pairs shortest path problem, longest common subsequence
8. Optimal binary search tree
9. N-queens problem using backtracking
10. TSP, Assignment Problem using branch and bound

Total P:60

0 0 4 2

1.   Download the datasets from UCI machine learning repository / www.kaggle.com for classification and clustering.
 a. Mail spam
 b. Breast cancer data
 c. Iris data
 d. MNIST dataset

2.   Implement the following Classification algorithms on the above suitable datasets.
 a. Naïve Bayes  
 b. LDA / QDA
 c. SVM
 d. K nearest neighbor
 e. Multi layer Perceptron

3.Apply tenfold cross validation experiments and statistical validation using t-test and ANOVA.
4. Apply clustering for image segmentation and image compression.
5. Apply Spectral clustering on data sets and visualization through plots
6. Apply PCA / LDA / Factor analysis on Iris data set, reduce the dimension and visualize the data.
7. Apply semi supervised learning techniques on data sets for the following tasks: to fill missing values / classification
8. Evaluation of ML models and fine-tuning of hyperparameters using any AUTOML tools / Weights & Biases.
9. Develop a user interface and deploy ML models in cloud.

Total P:60

JAVA PROGRAMMING: Introduction - Data Types - Operators - Declarations - Control Structures - Arrays and Strings - Input/Output - Java Classes - Fundamentals - Methods - Constructors - Scope rules - this keyword - Object Based vs Oriented Programming – Inheritance - Reusability - Composing class - Method overloading - Abstract classes - Dynamic Method Dispatch.

PACKAGES AND INTERFACES: Packages - Access protection - Importing packages - Interface - Defining and Implementing Interface - Applying Interface - Variables in Interfaces

EXCEPTION HANDLING: Fundamentals - Exception types - Uncaught Exception - Using Try and Catch - Multiple catch clauses - Nested Try statements - Throw - Throws - Java Built-in Exception - Chained Exceptions - Creating your own subclasses.

MULTI THREADED PROGRAMMING: Java thread model - Priorities - Synchronization - Messaging - Thread class and runnable Interface - Main thread - Creating the Thread - Synchronization – Inter-thread Communication – Deadlock.

COLLECTIONS FRAMEWORK: Generics – Autobox – Auto unboxing – Collections Frameworks – List – Set – Queue - Map

I/O AND GUI DEVELOPMENT: I/O basics - Stream - Stream Classes - Object Serialization – NIO Classes - Applet fundamentals - AWT - Swing – JavaFX Basics – UI Controls – Event Driven Programming.

NEW FEATURES: Annotations - Functional Programming - Lambda expressions

PRACTICALS:
1. Implementation of runtime polymorphism using abstract class and interface.
2. Implementation of callback feature using interface.
3. Illustrate a program for interface inheritance.
4. Implementation of user defined packages.
5. Illustrate a program for user defined exception, checked exception and unchecked exception.
6. Implementation of threads, thread groups, inter-thread communication using shared memory, piped stream.
7. Implementation of Files and I/O handling.
8. Implementation of Collections Framework API’s.
9. GUI Development using JAVA FX.
10. Implementation of JDBC API.

REFERENCES:
1. Herbert Schildt, “Java: the Complete Reference”, McGraw Hill, 2021.
2. Joyce Farrell, “Java Programming”, Cengage Learning, 2019.
3. Paul J. Deitel and Harvey Deitel, "Java 9 for Programmers", Pearson Education, 2018.
4. Y Daniel Liang, “Introduction to Java Programming”, Pearson Education, 2019.

Total P:60

3 0 0 3

INTRODUCTION: Various Computing Paradigms - Cloud Computing Overview: Characteristics – challenges – benefits – limitations - Evolution of Cloud Computing - cluster computing Grid computing, Grid computing versus Cloud Computing - Serverless computing – AWS Lambda – Firebase Cloud Functions. - Cloud computing architecture- Cloud Reference Model (NIST Architecture) - open group cloud ecosystem reference model (7)

VIRTUAL MACHINES: Process and System VMs - Taxonomy of VMs - Types of Virtualization: Hardware Emulation - Full Virtualization with binary translation - Hardware assisted - Operating System Virtualization - OS assisted /Para virtualization (5)

HYPERVISOR AND VM PORTABILITY: Hypervisor - Type 1, Type 2 Para-virtualization - Server Virtualization - Desktop Virtualization - VM portability Clones – Templates - Snapshots – OVF - Hot and Cold Cloning - Protecting & Increasing Availability - Light Weight Virtual machine: Container / Docker (7)

CLOUD SERVICE MODELS: Service Models –Characteristics – Benefits - Enabling Technologies (IaaS/PaaS/SaaS) - Case Study: SaaS: Salesforce.com, Online Collaboration Services IaaS : AWS, OpenStack PaaS : IBM Bluemix, GAE (5)

CLOUD DEPLOYMENT MODELS AND RESOURCE MANAGEMENT: Public/Private/Multi-cloud deployments - Hybrid Cloud - Community Cloud - Shared Private Cloud - Dedicated Private Cloud - Dynamic Private Cloud - Shared Resources – Resource Pool –Usage and Administration Portal – Benefits of Cloud models - Savings and cost impact - Resource Management – Elastic Environment – Resilient Environment–Security – Workload Distribution – Dynamic provisioning - AWS Spot Instances - Web services delivered from cloud (5)

KUBERNETES: Docker – Containers and images – Container Orchestration – Pods – Deployments – Services – Storage – Secrets. (3)

CLOUD ECO SYSTEMS: The concept of a cloud ecosystem - Actors and Roles in the Cloud Eco System - Cloud adoption vision - identifying your use cases - developing your plan - Understanding the implications of Cloud Service Layers - Utilizing cloud to gain strategic advantage (4)

CLOUD DATA CENTERS: Historical Perspective - Datacenter Components - Design Considerations - Power Calculations - Evolution of Data Centers (4)

APPLICATION DEVELOPMENT FRAMEWORK: Accessing the clouds: Web application vs Cloud Application - Frameworks: Model View Controller (MVC) - Struts – Spring - Cloud platforms in Industry – Google AppEngine - Microsoft Azure – Openshift – CloudFoundry – Cloud Orchestration – Cloud pricing models – finding right cloud provider (5)

Total L: 45

TEXT BOOKS
1. Rajkumar Buyya, Christian Vecchiola, ThamaraiSelvi S, “Mastering Cloud Computing”, McGraw Hill, 2022.
2. Dan C. Marinescu, "Cloud Computing: Theory and Practice", Morgan Kaufmann, 2022.
3. Thomas Erl, Robert Cope, Amin Naserpour, "Cloud Computing Design Patterns", Pearson, 2017.

REFERENCES
1. Thomas Erl, Zaigham Mahmood, Ricardo Puttini, “Cloud Computing: Concepts, Technology and Architecture”, Pearson, 2019.
2. Kai Hwang, Min Chen, “Big Data Analytics for Cloud, IoT and Cognitive Computing”, Wiley, 2018.

3 0 0 3

INTRODUCTION – Basic concepts – Convex sets, convex functions – loss functions – Gradient descent – Variants - Perceptron – Activation functions - Geometric representation – Perceptron Convergence theorem (3)

FEED FORWARD NETWORKS - Multi layer Perceptron – back propagation - Learning XOR – Auto encoder - Deep neural networks (7)

TRAINING NEURAL NETWORKS: Optimization methods for neural networks - Adagrad, Adadelta, rmsprop, adam, NAG - second order methods for training, Saddle point problem in neural networks, Regularization methods - dropout, batch normalization, Ridge and Lasso (10)

CONVOLUTIONAL NETWORKS –structure – properties – Region based CNN - LeNet – Alex net (5)

RECURRENT NETWORKS – Recurrent neural networks(RNN) – Gated Recurrent unit – Long Short Term Memory - Bidirectional RNNs - Deep recurrent network – Methodology – Applications (8)

DEEP LEARNING RESEARCH : Linear Factor Models, variants of Autoencoders, Representational Learning, Structured probabilistic models for deep learning, Monte Carlo Methods, Generative adversarial networks - Deep generative models   (9)

APPLICATIONS : Natural language processing, Big Data, Brain Computer Interface, Vision, IoT   (3)

Total L:45

TEXT BOOKS:
1. Ian Goodfellow, Yoshua Bengio, and Aaron Courville , “Deep Learning”, The MIT Press, 2016,
2. Yoshua Bengio, “ Learning Deep Architectures for AI, Foundations & Trends in Machine Learning”, Now Publishers, 2009

REFERENCES
1. Li Deng, Dong Yu, “Deep Learning: Methods and Applications”, Now Publishers 2014.
2. Jon Krohn, “Deep Learning for Natural Language Processing: Applications of Deep Neural Networks to Machine Learning Tasks”, Addison-Wesley, 2017.

3 0 0 3

OBJECT AND SPATIAL DATABASES: Object Oriented Databases - Complex data types - Structured types and Inheritance - Query Processing in Object databases - Spatial Databases: Geometric Information System - Spatial Data Types – Spatial Queries - Spatial indexing techniques. (6)

PARALLEL AND DISTRIBUTED DATABASES: Architecture of parallel databases – Parallel query evaluation, Parallel query optimization – Distributed DBMS Architecture, Distributed Database Design, Distributed Query Processing. (5)

DATA MODELING FOR BIG DATA: Big Data and Challenges, Big Data models, NoSQL data models, Principles of NoSQL models, BASE properties, CAP Theorem (5)

NOSQL DATABASES: Document and Graph Databases: Document Oriented Stores – MongoDB - Graph databases: Neo4J (8)

NOSQL DATABASES: Key Value and Columnar Databases: Key -Value Stores (in-memory) :Redis , Column Oriented Store: Cassandra - Hbase - BigTable (8)

BIG DATA PLATFORM: Hadoop and HDFS-Map-reduce - SPARK- Real time Streaming (8)

DATABASE INTEGRATION: Data warehousing, Virtual Data Integration - Schema directed data integration - Schema mapping and information preservation-Applications (5)

Total L:45

TEXT BOOKS
1. M.TamerOzsu, Patrick Valduriez, “Principles of Distributed Database Systems”, Springer,2019
2. Tomasz Wiktorski, “Data-intensive Systems: Principles and Fundamentals using Hadoop and Spark (Advanced Information and Knowledge Processing)”, Springer, 2019
3. Pramod J. Sadalage and Martin Fowler, “NoSQL Distilled - Brief Guide to the Emerging World of Polyglot Persistence”, Pearson, 2013
REFERENCES
1. ElmasriRamez and Navathe SB, “Fundamentals of Database Systems”, Pearson, 2017.
2. Anhai Doan, Alon Halevy, Zachary Ives, “Principles of data integration”, Morgan Kaufmann, 2012.

3 2 0 4

INTRODUCTION: The foundations of AI - The History of AI - Intelligent agents - Agent based system. (2)

PROBLEM SOLVING: State Space models - Searching for solution - Uninformed search – Heuristics: Functions--properties- Informed search – Greedy best first search -A* search – Local Search algorithms: Hill-climbing search - Genetic Algorithm - Adversary based search: Minimax – Alpha Beta pruning-Imperfect real time decisions: Cutting-off search-Stochastic games : Expectimax – Constraint satisfaction problem: Inference- Backtracking search. (16)

KNOWLEDGE REPRESENTATION AND REASONING: Knowledge representation - Logics - bivalent logic - inference - Fuzzy logic: membership - Fuzzy rules and reasoning - Fuzzy inference. (8)

UNCERTAIN KNOWLEDGE AND PROBABILISTIC REASONING: Uncertainty - Probabilistic reasoning: Semantics of Bayesian network - Exact inference in Bayesian network- Approximate inference in Bayesian network - Probabilistic reasoning over time: Inference in temporal models - Hidden Markov Models – Dynamic Bayesian Networks. (10)

DECISION-MAKING: Basics of utility theory –Decision Networks- Sequential decision problems - Markov decision process - Value iteration - Policy iteration - Decisions in Multi agent system: Single move games - Group decision making. (9)

TUTORIAL PRACTICE:
1. Search Techniques: A* algorithm for 8 – puzzle and Missionaries and Cannibals problem, Hill climbing, genetic algorithm and Constraint satisfaction techniques 2. Simple games – minimax and expectimax
3. Logic based exercises, Fuzzy Inference System.
4. Decision making: Implementing HMM models, sequential and multi agent decision making

Total: L: 45+T: 30 = 75

TEXT BOOKS:
1. Stuart Russell and Peter Norvig, “Artificial Intelligence: A Modern Approach”, Pearson Education, 2020.
2. David Pool and Alan Mackworth, “Artificial Intelligence: Foundations of Computational agents”, Cambridge University Press, 2017.
3. Timothy Ross, “ Fuzzy Logic with Engineering Applications”, John Wiley and sons, 2016.
REFERENCES:
1. Christopher M.Bishop, “Pattern Recognition and Machine Learning”, Springer, 2016.
2. Daphne Koller and N Friedman, “Probabilistic Graphical Models - Principles and Techniques”, MIT press, 2009

0 0 4 2

Working with AWS-ECS,EKS,Lamda,S3,Pipelines • OpenStack
• Containers: Working with docker
• PaaS: Working with Heroku
• NoSQL: CouchDB
• Load Balancing with Nginx
• Designing MapReduce Applications
• Openshift
• Azure Cloud services
• TOSCA service deployment modeling

Total P: 60

0 0 4 2

1. Designing applications using object relational databases
2. Designing and querying spatial databases
3. Designing No-SQL databases: MongoDB, Redis, Cassandra, HBASE, Neo4J.
4. Parallel programming using Map-Reduce -Hadoop
5. Big Data Analytics using Spark
6. Application building using data integration tools

Total P:60

0 0 4 2

1. Collect data sets from the url : http://deeplearning.net/datasets/
2. Use TensorFlow library for visualization of data sets in different domains and analysis:
 a. Given a set of images of handwritten digits from MNIST, classify the images into digits
 b. Do image captioning using RCNN
 c. Text classification using CNN
 d. Language modeling using RNN
 e. Speech processing
 f. Optical character recognition using CNN and RNN
 g. Sentiment analysis and classification using LSTM
 h. Document classification / Radiology reports classification
 i. Visualization of CNN and RNN parameters
 j. POS tagging using RNN
 k. Dimensionality reduction and visualization using Auto encoders and its variants
 l. Develop user interface and deploy deep learning models
 m. Evaluation of deep learning models and hyperparameter tuning using Auto ML / Weights and Biases

0 0 0 12

3 0 0 3

REINFORCEMENT LEARNING PROBLEM: Interactive ML - Sequential decision making –- RL Framework - Exploration Exploitation Dilemma - Goals and Rewards (3)

MULTI ARM BANDITS: K Armed Bandit problem – Definition – Uses – MAB Algorithms: Greedy, UCB, PAC – Thompson Sampling - Contextual Bandits (6)

MARKOV DECISION PROCESS (MDP): Markov Reward Process – Definition of MDP - POMDP – Returns - Policies - Value functions – Bellman Equations - Optimality in MDP (5)

EXACT SOLUTION METHODS: Dynamic Programming: Policy Evaluation – Policy Improvement - Value Iteration, Asynchronous DP- Efficiency of DP - Stochastic DP ; Monte Carlo: Model based Vs Model free approaches - Policy Evaluation - Policy Improvement- On-policy and Off- policy Monte Carlo controls - Incremental implementation – Temporal Difference Learning: TD-prediction- Optimality of TD – SARSA - Q-Learning – Expected SARSA – n-step TD- Eligibility traces (13)

FUNCTION APPROXIMATION: Tabular methods Vs Parameterized functions - Value function approximation – Stochastic gradient methods - Linear methods – Artificial Neural Network – Deep Q Networks, Experience Replay – Lazy learning (10)

POLICY GARDIENT METHODS: Policy Approximation - REINFORCE algorithm, Estimating gradients, Actor-Critic methods. (5)

PLANNING AND LEARNING: Model and planning – Dyna Q - Prioritized sweeping - Heuristic search: Monte Carlo Tree search (3)

Total L: 45/p>

TEXT BOOKS:
1. Sutton R. S. and Barto A. G., "Reinforcement Learning: An Introduction", MIT Press, 2018.
2. Lattimore, T. and Szepesvári, C.” Bandit Algorithms”, Cambridge University Press, 2018.
3. Dimitri P. Bertsekas, "Reinforcement Learning and Optimal Control”, Athena Scientific, 2019
4. CsabaSzepesvári, “Algorithms for Reinforcement Learning”, Morgan & Claypool, 2010.

REFERENCES:
1. Masashi Sugiyama, “Statistical Reinforcement Learning: Modern Machine Learning Approaches”, CRC Press, Taylor & Francis Group, 2015
2. Stuart Russell and Peter Norvig, “Artificial Intelligence: A Modern Approach”, Pearson, 2020

3 0 0 3

INTRODUCTION: Analysis in NLP: morphological – syntactic, semantic -  pragmatic – Applications –  Morphological parsing - Regular expressions - Finite state automata - Finite state transducer  - Syntax Analyser - context free grammar,  CYK Parser, EarleyParser - Probabilistic CFG  - Dependancy Parsing - Semantic Analysis –Lexical semantics -  Syntax directed semantic analysis – semantic  similarities – word net based similarity -  Word sense disambiguation, Machine learning based approaches (15)

PART OF SPEECH TAGGING (POS):Rule based, HMM based POS tagger, Brill Tagger, Maximum entropy model, Maximum Entropy Markov model                                                                                                                                                                (10)

LANGUAGE MODELS:N gram models - Smoothing  Techniques, Laplacian, Add-One, Good Turing, Interpolation, Backoff -   Evaluating language models – Perplexity(6)

MACHINE TRANSLATION:Rule base translation - Statistical machine translation – Parameter learning in IBM models using EM -  Neural Machine translation(4)

INFORMATION EXTRACTION:Named Entity Recognition (NER) – Relation Extraction  - Natural Language generation – Topic modelling using Latent Dirichlet Allocation (LDA), Non-Negative Matrix Factorization (NMF)                                            (5)

DEEP LEARNING ARCHITECTURES:Attention models - Transformers - BERT - Applications  - Question answering , Classification, Summarization, Chatbot (5)

TUTORIAL PRACTICE:
1. Sentiment analysis and classification using n gram models, RNN andLSTM
2. Document classification / Radiology reports classification using RNN and LSTM
3. Visualization of text data
4. POS tagging on text data using HMM
5. Language modeling using n gram models
6. Machine translation using Deep learning and HMM
7. Optical character recognition using
8. Word sense disambiguation

Total L: 45

TEXT BOOKS:
1. Daniel Jurafsky and James H. Martin, “Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics and Speech Recognition”, Prentice Hall, 2014.
2. Philipp Koehn , “Statistical Machine Translation”, Cambridge University Press, 2010.

REFERENCES:
1. Christopher Manning and Hinrich Schütze, “Foundations of Statistical Natural Language Processing”, MIT Press, 2008.
2. James Allen, “Natural Language Understanding”, Addison Wesley, 1995.

3 0 0 3

INTRODUCTION:Key factors of Deployable ML (3)

EXPLAINABILITY AND INTERPRETABILITY OF BLACK BOX MODELS:Importance of interpretability in ML - Overview of simple interpretable models (linear regression, decision trees) -Model agnostic interpretations -example-based explanations; Local explanation models; Global Explanation Models; Theoretical aspects of explainability. (11)

CAUSAL MACHINE LEARNING:Review of Graphical Models, Structural Causal Models, Pearl’s do-calculus, Confounders, Counterfactuals, Causal discovery from data, Intervention based Causal discovery, Causal representation learning (9)

ALGORITHMIC FAIRNESS IN MACHINE LEARNING:Need for Fair ML, Metrics of fairness - Group fairness: Equality of odds, Demographic parity, Predictive rate parity. Individual fairness, Counterfactual fairness. Impossibility results. (10)

PRIVACY PRESERVING & FEDERATED MACHINE LEARNING:Statistical notions of Private Machine Learning - Differential Privacy – Federated Machine Learning: Machine learning in horizontally/vertically partitioned data –collaborative fairness in federated learning - privacy related challenges (12)

Total L: 45

TEXT BOOKS:
1. Molnar, Christoph. “Interpretable machine learning”. Lulu. com, 2020.
2. Solon Barocas and Moritz Hardt and Arvind Narayanan, “Fairness and Machine Learning”, Fairmlbook.org, 2019.
3. Dwork, Cynthia, and Aaron Roth. "The algorithmic foundations of differential privacy” Foundations and Trends in Theoretical Computer Science 9.3-4 (2014): 211-407.
REFERENCES:
1. Judia Pearl, Madelyn Glymour, Nicholas P Jewell, “Causal Inference in Statistics-A Primer”, Wiley , 2016.
2. Qiang Yang, Yang Liu, Yong Cheng, Yan Kang, Tianjian Chen, Han Yu, Federated Learning, Morgan& Claypool, 2019

0 0 4 2

1. N-armed Bandit problem – Recommender systems.
2. Frozen lake problem – Monte Carlo methods
3. Focused Crawler – TD approaches 
4. Finding shortest paths in graphs using RL.
5. Solving GridWorld problems.
6. Shape Completion - DeepRL
7. Games – Atari, AlphaGo

Total P: 60

0 0 4 2

Sample lab assignments 1. Finding causal relationships in data
2. Providing explanations for predictions (Recommendation systems)
3. Applications for Data Privacy through differential privacy
4. Applications for Data Privacy through federated learning
5. Case studies for fairness algorithm

Total P: 60

0 0 4 2

A capstone project is a multi-faceted body of work that serves as a culminating academic and intellectual experience for students. The list of activities is as follows:
a. Introduction
b. Literature review
c. Methodology
d. Results
e. Discussion

Total P: 60

3 0 0 3

INTRODUCTION: Security Problems in computing – security goals –threats and attacks. – Services and mechanisms.(2)

CRYPTOSYSTEMS: Introduction– symmetric key cryptography-substitution cipher – transposition cipher -stream ciphers and block ciphers – Advanced Encryption Standard (AES) – cryptanalysis of symmetric key cryptosystems. Public key cryptography -Introduction – RSA cryptosystem- attacks on RSA – Elliptic curve cryptosystem(10)

MESSAGE INTEGRITY, AUTHENTICATION AND KEY MANAGEMENT: Message digest – Message authentication code – Cryptographic hash function – Digital signatures- Anonymous protocols- Challenge response system- zero knowledge protocol- secure two party computation-DSS. Symmetric key distribution – kerberos- Diffie – Hellman key agreement– Public key distribution – Certificates.(10)

NETWORK SECURITY: Application Layer Security – PGP and S/MIME, Transport Layer security – SSL - Network layer security – IPSec.(4)

PROGRAM SECURITY : Secure Coding – Malicious and non-Malicious program errors -OWASP/SANS Top Vulnerabilities - Malwares – types - Buffer Overflows – defense mechanisms- Incomplete mediation - XSS - Redirection - Inference – Application Controls - Evaluation of Security Systems(4)

DATA BASE SECURITY : Security Requirements – database administration security – SQL injection and exploitation and defense methods - database roles and permissions – Object level security - Sensitive data – Multilevel Databases – Multi level security. (5)

DATA PRIVACY : – Foundations of privacy- Logical methods for specification and enforcement of privacy policies- Privacy preserving data publication- An introduction to Differential privacy: Definitions and early uses – Noiseless differential privacy- Applications of Differential privacy – Synthetic datasets and network trace analysis- Differential privacy for large data- Differentially private social network analysis – Web privacy: online tracking and advertisement – Privacy and machine learning – case study: HIPAA privacy rule(10)

Total L: 45+T:30 = 75/p>

TEXT BOOKS:
1. John R. Vacca, “Network and Systems Security” Syngress Imprint of Elsevier, 2014.
2. Behrouz A.Forouzan and DebdeepMukhopadhyay, “Cryptography and Network Security”, Tata McGraw Hill, 2011.
3. Mark Stamp, “Information Security: Principles and Practice”, John Wiley& Sons, 2011.

REFERENCES:
1. Basta, Alfred, and Melissa Zgola. “Database Security”. Cengage Learning, 2011. 2. Cynthia Dwork and Aaron Roth, “The Algorithmic Foundations of Differential Privacy,” Nova publishers Inc, 2014.

3 0 0 3

INTRODUCTION:Motivation for Data Mining – Importance – Definition – Kinds of data for  Data Mining – Data Mining functionalities – Patterns – Classification of Data Mining Systems – Major issues in Data Mining-Overview of  Data Mining Techniques. Data preprocessing: Types of data, Data cleaning-Smoothing, Handling missing values- Data reduction- Feature subset selection – Sampling methods -Data transformation-Data discretization-Chi square and Information Gain                 (9)

DATA WAREHOUSE and OLAP TECHNOLOGY:Overview- Need for Data Warehouse- multidimensional data model-Data Warehouse architecture -Data warehousing Schemas - Data Warehousing to Data mining .                                              (6)

MINING FREQUENT PATTERNS, ASSOCIATIONS AND CORRELATIONS:Basic concepts – Efficient and Scalable Frequent Itemset Mining methods – Apriori, FP-Growth, Eclat. CLASSIFICATION AND PREDICTION :Overview of Classification techniques–Ensemble Learning-bagging, boosting, cascading, stacking –CLUSTERING-Hierarchical-Density- based.

MINING DATA STREAMS:Challenges- Characteristics of Streaming Data, Issues and Challenges, Streaming Data Mining Algorithms, Any time stream Mining.                                                       (6)

SEQUENCE MINING:Characteristics of Sequence Data, Problem Modeling, Sequential Pattern Discovery,  Timing Constraints, Applications in Bioinformatics, MULTIVARIATE TIME SERIES (MVTS) MINING:  Importance of MVTS data - Sources of MVTS data -  Mining MVTS data.                   (8)

APPLICATIONS AND TRENDS IN DATA MINING:Spatial Data Mining –Graph Mining- Web Mining –Text Mining. (6)

Total L: 45

TEXT BOOKS:
1. Jiawei Han and Micheline Kamber , “Data Mining – Concepts and Techniques”, Morgan Kaufmann  Publishers, 2012.
2. Tan, Steinbach and Kumar, “Introduction to Data Mining”, Pearson Education, 2014.

REFERENCES:
1. Trevor Hastie, Robert Tibshirani and Jerome Freidman, “The Elements of Statistical Learning: Data Mining, Inference, and Prediction”, Springer, 2011.
2. Ian Witten, Frank Eibe and Mark A Hall, "Data Mining: Practical Machine Learning Tools and Techniques”, Elsevier, 2011.

3 0 0 3

INTRODUCTION: Overview of IR Systems - Historical Perspectives - Goals of IR - The impact of the web on IR

TEXT REPRESENTATION & RETRIEVAL MODELS: Statistical Characteristics of Text: Zipf's law, Heap’s Law - Data Structures for IR: Indexing techniques - Boolean Matching, Vector Space Models: TF-IDF weighting – Notion of similarity - LSI - Binary independence model (8)

LEARNING TO RANK IN IR:Learning to Rank framework - Neural Nets for IR - Co-occurrence matrix – Word Embedding: Glove, Fasttext, Doc2Vec; Cross-language information retrieval -Topic Modelling: LDA – Deep Learning for IR – RNN, BERT

QUERY MODELING& EVALUATION:Query Expansion – Feed back in Vector Space Model-Evaluation metrics: Recall, Precision and F- measure- Precision @k, R-Precision, Interpolated Precision, PR graph, Mean Average Precision, NDCG    (3)

WEB SEARCH:Web IR - Search engines - Crawl architecture - Duplicate detection – Min-hashing - Link Analysis: Random walk model - PageRank – HITS

WEB SEARCH: Systems and the WWW - Search Engines: Spidering, Meta Crawlers and near duplicate pages, Question answering,; Link analysis: Hubs and Authorities, Google PageRank, Duplicate Detection.(5)

KNOWLEDGE GRAPH & INFORMATION EXTRACTION:Key Concepts– Entity Recognition, Relation Extraction- Extracting information from text - Co-reference resolution

INFORMATION FILTERING:Recommender System – types - Neighbourhood based approaches – Latent factor models – NNMF – Handling incomplete data – Multi-objective recommender systems- Sequential recommendations                             (8)

Total L: 45

TEXT BOOKS:
1. Christopher D. Manning, Prabhakar Raghavan and Hinrich Schütze, “Introduction to Information Retrieval”, Cambridge University Press, 2012.
2. TommasoTeofili , “Deep Learning for Search”, Manning Publications, 2019
3. B.Croft, D. Metzler, T. Strohman, “Search Engines: Information Retrieval in Practice”, Pearson Education, 2015.


REFERENCES:
1. Mayank Kejriwal, Craig A. Knoblock and Pedro Szekely, “Knowledge Graphs”, MIT Press, 2021
2. Francesco Ricci, Lior Rokach, Bracha Shapira, Paul B. Kantor, “Recommender Systems – Handbook”, Springer, 2015.

0 0 4 2

1. Designing search engine – Desktop, Website 
2. Plagiarism Checker – Code Plagiarism & Document Plagiarism
  3. Developing Web crawler & Extracting data
4. Index creation and compression 
5. Recommendation Engine – Health care, E-commerce, E-learning 
6. Creating Knowledge Graph
7. DNA sequence matching

Total P: 60

0 0 4 2

1. Design of a Client server application for a basic cryptosystem.
2. Performing a frequency analysis attack on a cipher text enciphered with Affine cipher.
3. Detection of a Buffer overflow attack.
4. Packet Sniffing using Wireshark Tool to perform the traffic analysis attack.
5. Implementation of RSA cryptosystem.
6. Key distribution using RSA( KDC) – Key hacking.
7. Key exchange using Diffie-Hellman technique – MITM attack.
8. Authentication of File transfer using Hashing / Message digest.
9. Digital signature, generation and verification.
10. Password authentication.
11. Securing transaction by defending SQL Injection attacks.
12. Cross - Site scripting.
13. Implementation of security techniques to safeguard the database against accidental or deliberate breaches.
14. Security testing for applications.
15. Analysis and removal of vulnerabilities from a web application.

Total P: 60

0 0 4 2

1. Designing Desktop Search engine 2. Developing Web crawler & Indexing 3. Plagiarism Checker – Code Plagiarism & Document Plagiarism 4. Recommendation Engine 5. Creating Knowledge Graph 6. DNA sequence matching

Total P: 60

0 0 0 12

23XDA1 DATA COMPRESSION

3 2 0 4

DATA COMPRESSION LEXICON: Introduction to Data Compression - Dawn Age - Coding - Lossy Compression - Modelling and Coding (4)

INTRODUCTION TO INFORMATION THEORY:Minimum Redundancy Coding - The Shannon - Fano Algorithm, The Huffman Algorithm - Counting the Symbols, Building the tree.                                                     (4)

ADAPTIVE HUFFMAN CODING: Adaptive Coding - Updating the Huffman Tree - Escape code. (5)

ARITHMETIC HUFFMAN CODING: Arithmetic Coding with floating point data type – Arithmetic coding with integral data type. (6)

STATISTICAL MODELING: Higher-order Modeling - Finite Context Modeling – Order one modeling – Order two Modeling. (5)

DICTIONARY-BASED COMPRESSION: LZ77 Compression and Decompression - LZSS Compression and Decompression - LZ78 Compression and Decompression - LZW Compression and Decompression – LZMW Compression and Decompression - LZAP Compression and Decompression. (8)

IMAGE COMPRESSION Introduction-Types of Redundancies-Various approaches in image compression-JPEG Standard- JPEG 2000.

SPEECH COMPRESSION: Digital Audio Concepts - Lossless Compression of Sound. (5)

VIDEO COMPRESSION: JPEG Compression - Implementing DCT - Complete Code Listing. (5)

AUDIO COMPRESSION: Introduction – ADPCM - Psychoacoustic Model - Spectral masking - Temporal masking- MPEG Audio Coding.

DICTIONARY-BASED COMPRESSION: LZ77 Compression and Decompression - LZSS Compression and Decompression - LZ78 Compression and Decompression - LZW Compression and Decompression – LZMW Compression and Decompression - LZAP Compression and Decompression – LZY Compression and Decompression. (10)

TUTORIAL PRACTICE:
1. Implement Shannon Fano algorithm and Huffman algorithm.
2. Design compression and decompression program using adaptive Huffman coding.
3. Implement arithmetic coding algorithm.
4. Design compression program using statistical modeling upto 3 order.
5. Design compression and decompression program using LZ77 algorithm.

Total: L: 45+T: 30 = 75

TEXT BOOK:
1. Khalid Sayood, “Introduction to Data Compression”, Morgan Kaufmann, 2013.
2. David Salomon, “Data Compression: The Complete Reference”, Springer, 2014

REFERENCES:
1. Charles K. Chui, Qingtang Jiang, "Applied Mathematics: Data Compression, Spectral Methods, Fourier Analysis, Wavelets and Applications", Atlantic Press, 2013

3 2 0 4

THEORETICAL FOUNDATIONS: Function Spaces: Banach Spaces, Cauchy Sequences, Holder spaces, Sobolev spaces, reproducing kernel Hilbert spaces (RKHS), Concentration of Measure (10)

REGULARIZATION: Introduction - Ridge Regression, Lasso Regression. (8)

NONPARAMETRIC REGRESSION: Kernel Estimators, Polynomial Estimators, Linear Smoothers, Cross Validation, Data Splitting, Additive Models, SpAM algorithm (7)

LINEAR CLASSIFICATION: Review of Classification Models, Newton’s Method for Logistic Regression, Comparison of Logistic Regression with Linear Discriminant Analysis, Regularized Logistic Regression, SVM. (7)

NON-PARAMETRIC CLASSIFICATION: Plugin methods, k-NN, Boosting. (6)

MINIMAX RISK: Bounds of minimax risk, Le Cam’s method, Fano’s method, Tsybakov’s method, Hypercubes. (7)

TUTORIAL PRACTICE:

1. Ridge Regression and Lasso Regression for predictions.
2. Kernel PCA for non-linear datasets.
3. Nonlinear SVM using different kernel functions.
4. Classification using LDA and boosting.

Total L:45 + P:30 = 75

TEXT BOOKS:

1. Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani, “An introduction to Statistical learning”, Springer, 2013.
2. Trevor Hastie, Robert Tibshirani, Jerome Friedman, “Elements of Statistical Learning: Data Mining, Inference and Prediction”, Springer, 2013.

REFERENCES:

1. Vladimir N Vapnik, “Statistical learning theory”, Wiley, 1998.
2. Robert Schapire, Yoav Freund, “Boosting: Foundations and Algorithms”, The MIT Press, 2012.

OVERVIEW: Computer Imaging Systems: Image formation and Sensing, Color representation, Image Acquisition, Image digitization, Noise, Image Representation. (4)

DIGITAL IMAGE ANALYSIS: Pre-processing, Binary Image Analysis, Edge detection - First order derivative, Second order detection, Color edge detection, Pyramid edge detection, Edge linking and boundary detection. Segmentation - Region based segmentation, clustering techniques, thresholding. (8)

IMAGE ENHANCEMENT: Gray-Scale Modification, Histogram processing, Image Sharpening, Image Smoothing - Image Restoration - Noise Models, Noise removal using spatial filters, Geometric transforms, Image Reconstruction. (6)

IMAGE TRANSFORMS: Overview of discrete transforms, Fourier Transform, Discrete Cosine transform, Discrete Haar transform, Principal components transform, Discrete Wavelet Transform, Filtering in Frequency domain – Inverse filter, Weiner filter, Homomorphic filter, Least Squares filter. (6)

MORPHOLOGICAL OPERATIONS: Binary Dilation, Erosion, Opening and Closing, Hit-or-Miss Transform, Basic Morphological Algorithms, Extension to Gray-Scale Images. (4)

IMAGE FEATURE ANALYSIS: Overview, Feature Extraction - Shape, color, spectral, textural features, feature Analysis. (5)

VIDEO ANALYSIS: Video Acquisition, Detecting Changes, Background subtraction, Image Differencing, Tracking by Detection, Tracking Multiple Objects (5)

APPLICATIONS: Classification with machine learning and deep learning algorithms, Performance Measures. Real Time Applications: Object Detection, Biometric Authentication, Content Based Image Retrieval, Document processing, Pattern recognition methods, Face detection, Face recognition, Surveillance – foreground-background separation – human gait analysis Application, In-vehicle vision system: locating roadway – road markings – identifying road signs – locating pedestrians, Image based security. (6)

TUTORIAL PRACTICE:

1. Implementation of Viewing digital images, bits and bytes, sampling and quantization.
2. Apply scaling, translation and rotation, sums and differences with the grayscale and color images.
3. Implementation of Histograms and stretches, convolutional filters.
4. Implement Fourier transforms and the frequency domain, non-linear filters.
5. Construct edge detection algorithms using Operators.
6. Implement the morphological operations.
7. Apply various image encoding methods with grayscale images.
8. Implement various image segmentation methods.
9. Extract various image features and implement clustering and classification methods.
10. Developing simple image analysis applications.

TOTAL L:45

TEXTBOOKS:

1. Umbaugh, S. E., “Digital Image Processing And Analysis: Applications with Matlab and CVIPTOOLS”, CRC press, 2017.
2. Milan Sonka, VáclavHlavác; Roger Boyle, “Image Processing, Analysis, and Machine Vision”, Cengage Learning, 2015.

REFERENCES:

1. Richard Szeliski, “Computer Vision: Algorithms and Applications”, Springer, 2022.
2. Richard Hartley and Andrew Zisserman, “Multiple View Geometry in Computer Vision”, Cambridge University Press, 2014.
3. R.C. Gonzalez and R.E. Woods, “Digital Image Processing”, Addison- Wesley, 2017.
4. David A. Forsyth, Jean Ponce, “Computer Vision: A modern approach”, Prentice Hall, 2015.
5. Thomas B. Moeslund, “Introduction to Video and Image Processing”, Springer, 2012.

3 2 0 4

DIGITAL IMAGE PROCESSING: Elements of a Digital image processing system – Structure of the Human eye – Image formation and contrast sensitivity – Sampling and Quantization – Neighbours of a pixel – Distance measures – Photographic firm structure and exposure – Film characteristics – Linear scanner – Video camera – Image processing applications. (6)

IMAGE TRANSFORMS: Introduction to Fourier transform – DFT – Properties of two dimensional FT – Separability, Translation, Periodicity, Rotation, Average value – FFT algorithm – Walsh transform – Hadamard transform – Discrete Cosine transform. (5)

IMAGE ENHANCEMENT: Definition – Spatial domain methods – Frequency domain methods – Histogram modification technique – Neighborhood averaging – Media filtering – Lowpass filtering – Averaging of multiple images – Image sharpening by differentiation and high pass filtering. (8)

IMAGE RESTORATION: Definition – Degradation model – Discrete formulation – Circulant matrices – Block circulant matrices – Effect of diagnolization of circulant and block circulant matrices – Unconstrained and constrained restorations – Inverse filtering – Wiener filter – Restoration inspatial domain. (8)

IMAGE ENCODING: Objective and fidelity criteria – Basic encoding process – The mapping – The quantizer – The coder – Differential encoding – Contour encoding – Runlength encoding – Image encoding relative to fidelity criterion – Differential pulse code modulation. (8)

IMAGE ANALYSIS AND COMPUTER VISION: Typical computer vision system – Image analysis techniques – Spatial feature extraction – Amplitude and Histogram features. Transform features – Edge detection – Gradient operators – Boundary extraction – Edge linking – Boundary representation – Boundary matching – Shape representation. (10)

TUTORIAL PRACTICE:
1. Implementation of Viewing digital images, bits and bytes, sampling and quantization.
2. Apply scaling, translation and rotation, sums and differences with the grayscale and color images.
3. Implementation of Histograms and stretches, convolutional filters.
4. Construct edge detection algorithms using Operators.
5. Implement Fourier transforms and the frequency domain, non-linear filters.
6. Implement the image restoration techniques.
7. Apply various image encoding methods with grayscale images.
8. Implement the conversion between color spaces.
9. Extract the Spatial, Histogram, and Transform features.
10. Implement the boundary and shape representation of digital images.

Total: L: 45+T: 30 = 75

TEXT BOOKS:
1. Rafael C. Gonzalez and Richard E. Woods, “Digital Image Processing”, Prentice Hall, 2011.
2. Kenneth R. Castleman, “Digital Image Processing”, Pearson Education, 2007.

REFERENCES:
1. Maria Petrou , Costas Petrou, “Image Processing: The Fundamentals”, John Wiley& Sons, 2010.

3 2 0 4

MULTIMEDIA DATABASES: Architectures - Schema, Functional, System, Distributed, Interoperability, Hypermedia. Metadata for Multimedia Databases. (5)

DEALING WITH MULTIMEDIA DATABASES: Text Databases - Querying Character Data Using SQL, Statistical Methods for Text Analysis, Querying Multimedia Text, Content-dependent Metadata, Indexing Technologies for Text. Image Databases - Technologies for Image Processing, Role of Feature Extraction, Retrieval Methods, Developing Image Media Databases. Video Databases - Video Analysis and Segmentation, Storage of Video Objects, Dealing with Moving Images, Metadata for Speech and Video, Manipulating Video Data, Video Query Process. (10)

MULTIMEDIA DATA MINING: Technologies, Architectural Support, Process of Multimedia Data Mining, Outcomes, Approaches and Techniques. (5)

TEXT MINING - Overview, From Textural Information to Numerical Vectors - Collection Documents, Document Standardization, Tokenization, Lemmatization, Vector Generation for Prediction, Sentence Boundary Determination, Part-Of-Speech Tagging, Word Sense Disambiguation, Phrase Recognition, Named Entity Recognition, Parsing, Feature Generation. Using Text for Prediction - Recognizing that Documents Fit a Pattern, Document Classification, Learning to Predict from Text, Evaluation of Performance, Applications. (10)

DESIGNING A CONTENT-BASED IMAGE RETRIEVAL SYSTEM: Feature Extraction and Representation, Similarity Measurements, Dimension Reduction and High-dimensional Indexing, Clustering, The Semantic Gap, Learning, Relevance Feedback, Benchmarking Solutions. (7)

DESIGNING A CONTENT-BASED VIDEO RETRIEVAL SYSTEM: Video Parsing, Video Abstraction and Summarization, Video Content Representation, Indexing and Retrieval, Video Browsing Schemes, Samples of Video Retrieval Systems. (6)

AUDIO MINING: Overview, Audio Retrieval, Audio Mining, Taxonomy for Audio Mining. (2)

TUTORIAL PRACTICE:
1. Construct the following multimedia databases and manipulate them.
 a. Text Databases
 b. Image Databases
 c. Audio Databases
 d. Video Databases
2. Implement the various phases of text mining algorithm.
3. Construct a document classification system and analyze its performance.
4. Develop a CBIR system for a benchmark database with semantic and relevance feedback.
5. Construct an index based video retrieval system.
6. Develop an audio retrieval system.

Total: L: 45+T: 30 = 75

TEXT BOOKS:
1. Sholom M. Weiss, NitinIndurkhya, Tong Zhang and Fred Damerau, “Text Mining: Predictive Methods for Analyzing Unstructured”, Springer, 2010.
2. Oge Marques andBorkoFurht, “Content-Based Image and Video Retrieval”, 2012.
3. Robertson, L. Methods and innovations for multimedia database content management/current trends and future practices for digital literacy and competence, Information Science Reference, 2013.

REFERENCES:
1. Djeraba, C. Multimedia mining: a highway to intelligent multimedia documents (Vol. 22). Springer Science & Business Media, 2012.
2. Dunckley Lynne, Multimedia Databases: An Object Relational Approach, Pearson Education, 2003.

3 2 0 4

INTRODUCTION: Introduction and history of Computational Neuroscience - Tools and specialization in neuroscience – Levels of organization in the brain – Levels of analysis and Computational Theory of the brain. (3)

MATHEMATICAL BACKGROUND: Linear systems - eigenvalues, eigenvectors for symmetric matrices – quadratic forms, solving a system of linear equations - Dynamic systems, bifurcation map in terms of trace and determinant - Phase plane analysis: null clines - Hopf bifurcation and limit cycles . (11)

HODGKIN HUXLEY MODEL: Neuron - Membrane potential, action potential, Electrophysiology - Goldman-Hodgkin-Katz voltage equation - Hodgkin-Huxley model. (9)

COMPONENTS OF NEURAL SIGNALING – Neurotransmission - Models of Sensory Systems: Vision – Touch –Hearing -Motor Systems (4)

REVIEW OF ARTIFICIAL NEURAL NETWORKS: McCulloch-Pitts Neuron, Perceptron, MLP, Self-organizing map, Hopfield network.Neural Network: Perception – MLP: Back Propogation – case studies - Past tense learning, NetTalk, biological plausibility of backpropagation algorithm – Hebbian Learning and PCA - Linsker’s model of the visual system - Reinforcement Learning - Spiking neuron networks. (7)

INTRODUCTION TO INFORMATION THEORY: Communication channel and information gain – Information measure and Entropy – Properties of Joint and Conditional Information Measures and A Markov Source – Non-linear correlation measures. (5)

CASE STUDIES: Complex network analysis- Structural and functional brain networks. (6)

TUTORIAL PRACTICE:
1. Familiarity with tools such as EEGLab, UCINET.
2. Implementation of signal processing concepts – Frequency Component Analysis of signals etc.
3. Implementation of various Artificial Neural Network algorithms using real time neuroscience datasets.
4. Implementation of complex network metrics using UCI Net / PAJEK.
5. Statistical Analysis on Neuroscience data.

Total: L: 45+T: 30 = 75

TEXT BOOKS:
1. Eric Kandel, James Thomas Schwartz andJessel, “Principles of Neural Science”, McGraw-Hill, 2013.
2. FengJ., “Computational Neuro Science: A Comprehensive Approach”, Chapman and Hall / CRC, 2004.
3. Randall C. O'Reilly and Yuko Munakata, “Computational Explorations incognitive Neuroscience: Understanding the Mind” , MIT Press, 2000.

REFERENCES:
1. Thomas P. Trappenberg, “Fundamental of Computational Neuroscience”, Oxford University press, 2010.
2. RaymondW. Yeung, “Information Theory and Network Coding”, Springer, 2011.
3. CoverT. M. and ThomasJ. A., “Elements of Information Theory”, John Wiley& Sons, 2006.
4. Claude Elwood Shannon and Warren Weaver,“The Mathematical Theory of Communication”, University of Illinois Press, 1999.

3 2 0 4

OVERVIEW: Computer Imaging Systems: Image formation and Sensing, Color representation, Image Acquisition, Image digitization, Noise, Image Representation. (4)

DIGITAL IMAGE ANALYSIS: Pre-processing, Binary Image Analysis, Edge detection - First order derivative, Second order detection, Color edge detection, Pyramid edge detection, Edge linking and boundary detection. Segmentation - Region based segmentation, clustering techniques, thresholding. (8)

IMAGE ENHANCEMENT: Gray-Scale Modification, Histogram processing, Image Sharpening, Image Smoothing - Image Restoration - Noise Models, Noise removal using spatial filters, Geometric transforms, Image Reconstruction. (6)

IMAGE TRANSFORMS: Overview of discrete transforms, Fourier Transform, Discrete Cosine transform, Discrete Haar transform, Principal components transform, Discrete Wavelet Transform, Filtering in Frequency domain – Inverse filter, Weiner filter, Homomorphic filter, Least Squares filter. (6)

MORPHOLOGICAL OPERATIONS: Binary Dilation, Erosion, Opening and Closing, Hit-or-Miss Transform, Basic Morphological Algorithms, Extension to Gray-Scale Images. (4)

IMAGE FEATURE ANALYSIS: Overview, Feature Extraction - Shape, color, spectral, textural features, feature Analysis. (5)

VIDEO ANALYSIS: Video Acquisition, Detecting Changes, Background subtraction, Image Differencing, Tracking by Detection, Tracking Multiple Objects (5)

APPLICATIONS: Classification with machine learning and deep learning algorithms, Performance Measures. Real Time Applications: Object Detection, Biometric Authentication, Content Based Image Retrieval, Document processing, Pattern recognition methods, Face detection, Face recognition, Surveillance – foreground-background separation – human gait analysis Application, In-vehicle vision system: locating roadway – road markings – identifying road signs – locating pedestrians, Image based security. (6)

TUTORIAL PRACTICE:

1. Implementation of Viewing digital images, bits and bytes, sampling and quantization.
2. Apply scaling, translation and rotation, sums and differences with the grayscale and color images.
3. Implementation of Histograms and stretches, convolutional filters.
4. Implement Fourier transforms and the frequency domain, non-linear filters.
5. Construct edge detection algorithms using Operators.
6. Implement the morphological operations.
7. Apply various image encoding methods with grayscale images.
8. Implement various image segmentation methods.
9. Extract various image features and implement clustering and classification methods.
10. Developing simple image analysis applications.

Total L:45 + P30 = 75

TEXTBOOKS:

1. Umbaugh, S. E., “Digital Image Processing And Analysis: Applications with Matlab and CVIPTOOLS”, CRC press, 2017.
2. Milan Sonka, VáclavHlavác; Roger Boyle, “Image Processing, Analysis, and Machine Vision”, Cengage Learning, 2015.

REFERENCES:

1. Richard Szeliski, “Computer Vision: Algorithms and Applications”, Springer, 2022.
2. Richard Hartley and Andrew Zisserman, “Multiple View Geometry in Computer Vision”, Cambridge University Press, 2014.
3. R.C. Gonzalez and R.E. Woods, “Digital Image Processing”, Addison- Wesley, 2017.
4. David A. Forsyth, Jean Ponce, “Computer Vision: A modern approach”, Prentice Hall, 2015.
5. Thomas B. Moeslund, “Introduction to Video and Image Processing”, Springer, 2012.

3 2 0 4

MODELS AND METRICS: Marketing Analytics, Models and metrics- Market Insight – Market data sources, sizing, PESTLE trend analysis, and porter five forces analysis – Market segment identification and positioning. (8)

COMPETITIVE ANALYSIS AND BUSINESS STRATEGY: Competitor identification, Intelligence gathering, analysis and strategy- Analytics based strategy selection, with strategic models and metrics , Forecasting, balanced scorecard, and critical success factors. (8)

PRODUCT, SERVICE AND PRICE ANALYTICS: Conjoint analysis model, decision tree model, portfolio resource allocation, Pricing techniques, pricing assessment, pricing for business markets, price discrimination. (12)

DISTRIBUTION AND PROMOTION ANALYTICS: Retail location selection, distribution channel evaluation, and multi-channel distribution, Promotion budget estimation and allocation, promotion metrics for traditional media and social media.(8)

SALES ANALYTICS: E Commerce sales mode, sales metrics, profitability metrics and support metrics. (9)

TUTORIAL PRACTICE:
1 Implementation of different metrics in marketing analytics.
2. Implementation of forecasting techniques for sales data.
3. Implementation of portfolio analysis.

Total: L: 45+T: 30 = 75

TEXT BOOK:
1. Stephan Sorger, “Marketing Analytics – Strategic Models and Metrics”, Admiral Press, 2013.
2. Simon Kingsnorth, “Digital Marketing Strategy: An Integrated Approach to Online Marketing”, Kogan Page, 2019

REFERENCES:
1. Wayne L. Winston, “Marketing Analytics: Data-Driven Techniques with Microsoft Excel”, Wiley, 2014
2. Simon Kingsnorth, “Digital Marketing Strategy: An Integrated Approach to Online Marketing”, Kogan Page, 2019

3 2 0 4

GRAPHICS INPUT - OUTPUT DEVICES: Raster scan Displays - Random scan displays - Flat panel displays, Touch panels - LCD – IO Devices FOR Graphics Environment

GRAPHICAL USER INTERFACE AND INTERACTIVE INPUT METHODS: The user dialog - Input of graphical data - Input function - Interactive picture construction techniques - Virtual reality, Augmented Reality - Environments, Devices.

COLOR MODELS: Properties of light, Color Models, Standard Primaries and the Chromaticity Diagram, RGB Color Model, YIQ and related Color models, CMY and CYMK color models, HSV Color model, HLS Color Model

OPENGL: Architecture, The OpenGL API, Primitives and Attributes, Color, Viewing, Control Functions, Programming Event- Driven Input, OpenGL Extensions.

TWO DIMENSIONAL GRAPHICS: Basic transformations - Matrix representation and homogeneous coordinates - Composite transformations, 3D transformation - Line drawing algorithms: DDA and Bresenham's algorithms - Circle generation algorithms: Midpoint circle algorithm - Point clipping - Line clipping: Cohen Sutherland algorithm, Liang-Barsky algorithm - Polygon clipping: Sutherland Hodgeman algorithm, Weiler-Atherton clipping.

RASTER GRAPHICS: Fundamentals: generating a raster image, representing a raster image, scan converting a line drawing, displaying characters, speed of scan conversion, natural images - Solid area scan conversion: Scan conversion of polygons, Y- X algorithm, properties of scan conversion algorithms - Interactive raster graphics: painting model, moving parts of an image, feedback images.

CURVES AND SURFACES: Parametric representation of curves - Bezier curves – B-Spline curves - Parametric representation of surfaces - Bezier surfaces - Curved surfaces - Ruled surfaces - Quadric surfaces – Concatenation of two curve segments – Order of Continuity.

LIGHTING AND SHADING: Lighting model, shading model, material properties, light properties, light sources, Specifying lighting parameters - implementing a lighting model - shading of the sphere model - per-fragment lighting - global illumination

THREE DIMENSIONAL GRAPHICS: Viewing 3D graphical data - Orthographic, oblique, perspective projections - Hidden lines and hidden surface removal – Z-Buffer, A-Buffer, BSP Tree, Area-Subdivision algorithm.

FRACTAL-GEOMETRY METHODS: Tiling the plane - Recursively defined curves - Koch curves - C curves - Dragons - Space filling curves - Fractals - Grammar-based models - Graftals - Turtle graphics - Ray tracing.

Note: Algorithms have to be implemented using C++/OpenGL. Modeling of objects can be done through Unity

TUTORIAL PRACTICE:

1. Drawing a line, circle using algorithms.
2. Implementation of 2D Transformations (translation, scaling, rotation).
3. Window – viewport simulation with various aspect ratios.
4. Line clipping and Polygon clipping using algorithms.
5. Polygon Filling Algorithms.
6. Manipulation of raster images.
7. Drawing a 2D curve using Bezier and B-Spline generations.
8. 3D Projections – Orthographic and Perspective.
9. Hidden Surface Removal algorithm.
10. Mandelbrot and Julia Set Generation.
11. Graftals Generation.
12. Model a primitive (car / Aircraft).
13. Animate the primitive.

Total L: 45+T: 30=75

TEXT BOOKS:

• Donald Hearn and Pauline Baker M, “Computer Graphics with OpenGL", Pearson Education, 2014.
• William M. Newmann and Robert F Sproull, “Principles of Interactive Computer Graphics”, Tata McGraw Hill, 2014.

REFERENCES:

1. Edward Angel, “Interactive Computer Graphics: A Top-Down Approach using WebGL”, Pearson, 2015.
2. Francis S. Hill, Stephen M. Kelley , “Computer Graphics”, Pearson, 2015.
3. David F Rogers, “Procedural Elements for Computer Graphics”, 2nd Edition, McGraw Hill, 2017.
4. John F. Hughes, James D. Foley, “Computer Graphics: Principles and Practice”, Addison-Wesley, 2014.
5. Sumanta Guha, “Computer Graphics through OpenGL – From Theory to Experiments”, CRC Press, 2015.

3 2 0 4

BIOLOGICAL DATA: DNA, RNA, Amino acids, Protein, Structural databases, genomes, Central dogma – Molecular Biology, Prediction of molecular function and structure.

SEQUENCE COMPARISON ALGORITHMS: Dynamic Programming Algorithms, Edit Distance and Alignments, Alignment with Gap Penalties, Spliced Alignment, Similarity-Based Approaches to Gene Prediction, Multiple Alignment, HMM, Profile HMM Alignment, Viterbi Algorithm, Randomized Algorithms-Gibbs sampling, Genetic, Expectation Maximization Algorithm.

EXHAUSTIVE SEARCH ALGORITHMS: Repeat finding Hash tables, Exact, approximate, combinatorial pattern matching, profile search, Motifs, Motif finding using Greedy algorithm, Dynamic Programming Algorithms, Divide-and-Conquer Algorithms, Keyword Trees, Suffix Trees, Heuristic Similarity Search Algorithms, BLAST: Sequence against a Database.

GRAPH BASED ALGORITHMS: Shortest superstring problem – sequencing by hybridization – SBH as a Hamiltonian path problem – SBH as an Eulerian Path problem – Fragment assembly in DNA sequencing – Protein sequencing and Identification.

CLUSTERING ALGORITHMS: Support Vector Machine, Ant Colony Algorithm, Clustering and Trees, Hierarchical Clustering, k-Means Clustering, Evolutionary Trees, Distance-Based, Additive Matrices, parsimony Tree Reconstruction.

TUTORIAL PRACTICE:

1. Motif Finding.
2. Sequence Comparison.
3. Searching biological databases.
4. Applications of HMM.
5. Finding SBH using Hamilton cycle / Eulerian Path.
6. Implementation of some clustering algorithms.

Total: L: 45+T: 30 = 75

TEXT BOOKS:

1. Neil Jones and Pavel Pevzner, “An Introduction to Bioinformatics Algorithms”, MIT Press, 2009.
2. Jonathan Pevsner, "Bioinformatics and Functional Genomics", John Wiley, 2009.

REFERENCE:

1. Mount, “Bioinformatics: Sequence and Genome Analysis”, Cold Spring Harbor Laboratory Press, 2006.

3 2 0 4

INTRODUCTION TO MODELING: Modeling process, Overview of different kinds of model. (3)

EMPIRICAL MODELING WITH DATA FITTING: Error function, least squares method; fitting data with polynomials and splines. (6)

CAUSAL MODELING AND FORECASTING: Introduction, Modelling the causal time series, forecasting by regression analysis, predictions by regression. Planning, development and maintenance of linear models, trend analysis, modelling seasonality and trend, trend removal and cyclical analysis, decomposition analysis. Modelling financial time series. Econometrics and time series models. Non seasonal models: ARIMA process for univariate and multivariate (10)

PORTFOLIO MANAGEMENT: Simple market models, risk-free assets, risky assets, discrete time market model. Portfolio - Introduction, risk and return of a portfolio. Portfolio with two-securities- Risk and expected return, risk-reward analysis, asset pricing models, portfolio optimization, Markowitz model and efficient frontier method, Capital Asset Pricing Models (CAPM). (11)

MODELING WITH BIOINFORMATICS: Introduction, Biological data- types, mode of collection, documentation and submission. Sequence alignment- Definition, significance, dot matrix method, dynamic programming- Global and local alignment tools, scoring matrices and gap penalties. Multiple sequence alignment: Iterative methods. Genetic algorithm, Hidden Markovian models, statistical methods, position specific scoring matrices. (15)

TUTORIAL PRACTICE:
1. Implementing curve fitting techniques for data and error analysis
2. Causal models for financial time series data- trend analysis, seasonality indices identification, cyclical analysis
3. Implementing Portfolio optimization models- Risk award analysis,
4. Markowitz model and efficient frontier method for determining optimal portfolios.
5. Implementation of capital asset pricing model
6. Multiple sequence alignment using Hidden Markovian models.

Total: L: 45+T: 30 = 75

TEXT BOOKS:
1. Giordano F R, Weir M D and Fox W P,“A First Course in Mathematical Modeling”, Brooks/Cole, 2013.
2. Mount, DW, “Bioinformatics Sequence and genome analysis”, Cold Spring Harbor Laboratory, 2004.
3. Capinski M. and Zastawniak T, “Mathematics for Finance: An Introduction to Financial Engineering”, Springer, 2010

REFERENCES:
1. Hamdy A Taha, “Operation Research- An Introduction”, Pearson Education, 2014.
2. Christoffersen P, “Elements of Financial Risk Management”, Academic Press, 2012.
3. Alexander Isaev, Introduction to Mathematical Methods in Bioinformatics, Springer, 2006

3 2 0 4

SYSTEM METHODOLOGIES:  System - System Development - Types of systems – People involved in the systems development - The project life cycle models - Need for Software Engineering - Objectives and Benefits of Software Engineering - Factors that influence Quality & Productivity – Quality attributes of a software product - Software Development Models                (5)

SOFTWARE PLANNING: Software Project Estimation - Different techniques of Project cost estimation Decomposition techniques - COCOMO & PUTNAM models- SOFTWARE ANALYSIS: Functional and non-functional requirements- Requirements engineering process – Elicitation – validation and management -- Principles of Analysis - Analysis tools -Analysis  Models                          (8)

DESIGN CONCEPTS AND PRINCIPLES: Design process and concepts – Levels of Design - Coupling – Cohesion -Design Tools - Software Design Methods – Design Techniques - Design of Input and control - Design of Output.                                   (8)

SOFTWARE QUALITY and TESTING : Quality Assurance versus Quality Control-The Cost of Quality-Software Quality Factors - Importance –Testing Types -Testing Techniques- Test Design-Test Scenarios-Test Cases Design-types-Test Scenario-Test cases for sample case studies - Smoke Testing-Sanity Testing-Regression Testing, Re-Testing, Ad-Hoc Testing-Gorilla Testing. Test Management-Test Policy-Test Strategy-Test Plan-Test Process-Levels of Testing-Testing Metrics-Review-Walk through Inspection-Desk Checking - Testing Tools- Software Metric -Software quality - SEI CMM and ISO-9001 -Software Risk and Reliability (10)

OBJECT ORIENTED SYSTEMS DEVELOPMENT: Object Oriented Systems Development life Cycle - Object oriented methodologies -Rational Unified Process – Unified Modeling Language –Process workflows – Importance of Modeling – Types of Modeling-Software Design patterns- Business Aspects of Software Engineering.                                                               (8)

AGILE SOFTWARE DEVELOPMENT:- Traditional Model vs. Agile Model- Agile Fundamentals- Generating User Stories - Agile software development methods –Agile management  (6)  

TUTORIAL PRACTICE:Case Studies –-Software project Management- Model selection- Agile Frameworks- Scrum- Kanban- Lean Startup –Defect tracking and testing tools

Total: L: 45+T: 30 = 75

TEXT BOOKS:
1. Roger S Pressman, Bruce R Maxim “Software Engineering – A Practitioner’s Approach”,  McGraw Hill, 2019.
2. John Hunt, “The Unified Process for Practitioners”, Springer, 2014.
REFERENCES:
1. Boriz Beizer, “Software System Testing & Quality Assurance”, Thomson Publishing group, 2016.
2. Shari Lawrence Pfleeger, “Software Engineering Theory and Practice”, Pearson Education, 2017.
3. Grady Booch , James Rumbaugh and Ivar Jacobson , “The Unified Modeling Language User Guide”, Addison Wesley,2011.

3 2 0 4

BASIC CONCEPTS: Graphs – representations, Planar graphs- Euler’s formula, crossing number, doubly connected edge list data structure, Algorithm complexity – time and space. (4)

GEOMETRIC GRAPHS: Planar straight line graph, Euclidean minimum spanning tree algorithm, Art gallery problem, visibility graphs, Computing point visibility for polygons with and without holes. (10)

VLSI PHYSICAL DESIGN: Manhattan distance, overlap graph, containment graph, interval graph, neighborhood graph, hypergraphs, Rectilinear minimum spanning tree, Rectilinear Steiner minimum tree, Kernighan-Lin partitioning algorithm, Partitioning based algorithms for floor planning and placement, Lee’s algorithm for routing, Shadow propagation algorithm for compaction. (11)

VERTEX-PURSUIT GAME: Graph homomorphism, retracts, cops and robbers - cop number (k), bounds, cop-win graphs. Polynomial algorithm for fixed k, NP-hard with k not fixed. (10)

VORONOI DIAGRAMS: plane sweep algorithm, Voronoi Diagram – definition and properties, Fortune’s algorithm. Delaunay triangulation. (10)

TUTORIAL PRACTICE:
1. Storing planar graphs using doubly connected edge list.
2. Borůvka's Euclidean minimum spanning tree algorithm.
3. Computing point visibility for polygons with and without holes.
4. Polynomial time approximation algorithms for vertex guarding.
5. Vertex guard algorithm using set cover.
6. Kernighan-Lin partitioning algorithm.
7. Partitioning based algorithms for floorplanning and placement.
8. Lee’s algorithm for routing.
9. Shadow propagation algorithm for compaction.
10. Polynomial algorithm for fixed cop number.
11. Plane sweep algorithm.
12. Fortune’s algorithm for Voronoi diagram.
13. Divide and conquer Delaunay triangulation.

Total: L: 45+T: 30 = 75

TEXT BOOKS:
1. Thomas H. Cormen, Charles E. Leiserson and Ronald L. Rivest, “Introduction to Algorithms”, PHI Learning, 2013.
2. Ghosh S. K., “Visibility Algorithms in the Plane”, Cambridge University Press, 2007.
3. Naveed A. Sherwani, “Algorithms for VLSI Physical Design Automation”, Springer, 2013

REFERENCES:
1. Anthony Bonato, “The Game of Cops and Robbers on Graphs”, American Mathematical Society, 2011.
2. Mark de Berg, Otfried Cheong, Marc van Kreveld, “Computational Geometry - Algorithms and Applications”, Springer Verlag, 2011.

3 2 0 4

INTRODUCTION: Game theory the theory of rational choice – Interacting decision makers. (2)

NASHEQUILIBRIUM: Strategic games – Best response – Dominance – Examples from economics, business, environment, military - Symmetric games and symmetric equilibria. Illustrations: Cournot’s model of oligopoly, Electoral competition. (7)

MIXED STRATEGIES: Dominance – Equilibrium – Illustrations: Expert diagnosis, Reporting a crime – Formation of players’ beliefs. (4)

EXTENSIVE GAMES WITH PERFECT INFORMATION: Strategies and outcomes – Nash equilibrium – Subgame perfect equilibrium - Stackelberg’s model of duopoly, Buying votes – Illustrations: Entry into a monopolized industry, Electoral competition with strategic voters, Committee decision making. (7)

GAMES WITH IMPERFECT INFORMATION: Bayesian games – Examples – Strategic information – Transmission – Agenda Control with imperfect Information – Signaling games - Education as a signal of ability. (6)

REPEATED GAMES: The prisoner’s dilemma – Finitely repeated and infinitely repeated – Strategies – Nash equilibrium – Subgame – Perfect equilibria and the one – deviation – Property – General results – Finitely replaced games – Variation on a theme: Imperfect observability. (6)

INTRODUCTION TO ALGORITHMIC GAME THEORY: Auction and mechanism design basics - the Vickrey auction - Sponsored Search Auction - Social choice theory - VCG mechanism. Algorithmic Aspects of Equilibria: Existence and computational complexity equilibria - Market Equilibrium - Correlated Equilibrium. Quantifying the inefficiency of equilibria: Routing Games and Congestion Games - Network Formation - Price of Anarchy and Price of Stability - Bandwidth Sharing. (13)

TUTORIAL PRACTICE: 1. Identifying dominant and dominated strategies; Finding dominant strategy equilibrium and iterated elimination of dominated strategies equilibrium; Finding Nash equilibrium.
2. Finding best response functions for players; Finding Nash equilibria using best response functions; Finding Nash equilibria in mixed strategies.
3. Finding subgame perfect equilibria  (SPE) for extensive games with perfect information by backward induction; Finding all Nash equilibria by converting to normal form game and eliminate the Nash equilibria which are not subgame perfect equilibria.
4. Finding mixed strategies and behavioural strategies in extensive form games. Finding whether a mixed strategy is equivalent to a behavioral strategy and vice-versa. Finding equibria in Bayesian games.
5. Finding conditions under which grim-trigger strategy, limited-punishment strategy, and tit-for-tat strategies are Nash equilibria in repeated games.

Total: L: 45+T: 30 = 75

TEXT BOOKS:
1. Martin J. Osborne, “An Introduction to game theory”, Oxford University Press, 2004.
2. Nisan N., Roughgarden T.,Tardos E., Vazirani V., “Algorithmic Game Theory”, Cambridge University Press, Cambridge, 2007.
3. Vijay Krishna, “Auction Theory”, Academic Press, 2009.
REFERENCES:
1. Joel Watson, “Strategy: An Introduction to Game Theory”, W. W. Norton & Company, 2013.  2. Steven Tadelis, “Game Theory: An Introduction”, Princeton University Press, 2013.
3. David Easley, Jon Kleinberg, “Networks, Crowds, and Markets: Reasoning About a Highly Connected World”, Cambridge University Press,2010.
4. Matthew O. Jackson, “Social and Economic Networks”, Princeton University Press, 2008.
5. N. Nisan, T. Roughgarden, E. Tardos, V. Vazirani, “Algorithmic Game Theory”, Cambridge University Press, 2007

3 2 0 4

INTRODUCTION: On line Social Networks – Terminologies - Research topics. (3)

STATISTICAL PROPERTIES OF SOCIAL NETWORKS: Static properties – Dynamic properties. (5)

RANDOM WALKS IN SOCIAL NETWORKS – Random walks on graphs – Algorithms – Applications. (7)

COMMUNITY DISCOVERY IN SOCIAL NETWORKS: Communities – Core methods – Applications. (8)

NODE CLASSIFICATION IN SOCIAL NETWORKS: Methods using local classifiers – Random walk based methods – Algorithms - Applications - Node classification to large scale social networks. (10)

MODELS AND ALGORITHMS: Social influence analysis – Expert location in social networks – link prediction. (12)

TUTORIAL PRACTICE:
Do the following on Twitter, Facebook or any social network data set.
1. Study of the different metrics of social network.
2. Visualization of social network.
3. Power law distribution of social data.
4. Node classification using iterative method.
5. Node classification using label propagation.
6. Usage of Graph laplacian.

Total: L: 45+T: 30 = 75

TEXT BOOK:
1. Charu C. Aggarwal, “Social Network Data Analytics”, Springer Publications, 2011.
2. David Easley and Jon Kleinberg, “Networks, Crowds, and Markets: Reasoning About a Highly Connected World”, Cambridge University Press, 2010.

REFERENCES:
1. Marshall Sponder “Social Media Analytics Effective tools for building, interpreting, and using metrics”,McGrawHill, 2011.
2. Stanley Wasserman, Katherine Faust, “Social network analysis: Methods and applications “, Cambridge University Press, 1995.
3. Stephen Borgatti, Martin Everett, Jeffrey Johnson, “ Analysing Social Networks”, SAGE Publications Ltd., 2013.

3 2 0 4

RANDOM NETWORKS:Basics of networks and graphs, random network model - degree distribution, evolution, small world property, six degrees of separation, Watts-Strogatz model, local clustering coefficient, random networks and network science. (5)

SCALE-FREE PROPERTY:Power laws and scale-free networks, Hubs, Universality, Ultra-small property, role of the degree exponent, Generating networks with a pre-defined degree distribution. (10)

BARABÁSI-ALBERT MODEL:Growth and preferential attachment, Barabási-Albert model, degree dynamics, degree distribution, diameter and the clustering coefficient, preferential attachment - absence of growth, measure, non-linearity, the origins. (8)

EVOLVING NETWORKS:Bianconi-Barabási model, measuring fitness, Bose-Einstein condensation, evolving networks.(6)

DEGREE CORRELATIONS:Assortativity and disassortativity, Measuring degree correlations, Structural cutoffs, Degree correlations in real networks, Generating correlated networks, impact of degree correlations. (8)

NETWORK ROBUSTNESS: Percolation theory, robustness of scale-free networks, attack tolerance, cascading failures, modeling cascading failures, building robustness. (8)

TUTORIAL PRACTICE: 1. Implementation of Barabási-Albert model.
2. Implementation of Watts-Strogatz model.
3. Implementation of Bianconi-Barabási model.
4. Obtaining Degree correlations in real networks.
5. Case studies of the theory concepts on real networks.

Total: L: 45+T: 30 = 75

TEXT BOOKS:
1. Albert-László Barabási, MártonPósfai, Network Science, Cambridge University Press, 2016
2. FilippoMenczer, Santo Fortunato, Clayton A. Davis, “A First Course in Network Science”, Cambridge University Press, 2020.

REFERENCE:
1. Estrada, E., Fox, M., Higham, D.J. and Oppo, G.L.,“Network Science - Complexity in Nature and Technology”, Springer, 2010.
2.Ted G. Lewis, “Network Science: Theory and Practice”, John Wiley& Sons, 2013.

3 2 0 4

INTRODUCTION: Concepts and Terminology – Generic Processor / ASIC Processor Architecture – Pipeline Architecture – Instruction Set Architecture –modification to the von Neumann architecture - Types of Parallelism - Flynn's Classical Taxonomy –concurrent, parallel, distributed.(5)

PARALLEL COMPUTER MEMORY ARCHITECTURES: Shared Memory - Distributed Memory -Hybrid Distributed-Shared Memory Multiprocessors: Communication and Memory issues - Message Passing Architectures –UMA – NUMA - Vector Processing and SIMD Architectures – Memory consistency models. (5)

PARALLEL PROGRAMMING MODELS:  From Mud to Structure: Layers - Pipes and Filters - Blackboard, Interactive Systems: Model View Controller (MVC),Presentation Abstraction Control, Adaptable Systems – Reflection, Microkernel. Anti-Patterns.Case studies. (10)

DESIGNING PARALLEL PROGRAMS:  Automatic vs. Manual Parallelization - Understand the Problem and the Program - Partitioning -Communications - Synchronization -Data Dependencies - Load Balancing -Granularity -I/O -Limits and Costs of Parallel Programming - Performance Analysis and Tuning –speedup, efficiency – Amdahl’s Lab – Gustafson’s law – scalability – Parallel Examples -Array Processing - Compiler Transformation techniques for High performance computing: - Transformations for parallel Machines. (7)

PRAM ALGORITHMS& BSP: PRAM model of computation- Work-Time formalism and Brent’s Theorem; algorithm design techniques- parallel reduction, parallel prefix, pointer jumping, Euler tours, divide and conquer, symmetry breaking; survey of data-parallel algorithms; relative power of PRAM. (6)

HIGH PERFORMANCE COMPUTING ARCHITECTURES: Latency Hiding Architectures -Multithreading Architectures - Dataflow Architectures - GPGPU Architecture- Overview of basic Accelerators /GPU / GPGPU and its programming model – CUDA - OpenCL.(6)

DISTRIBUTED COMPUTING: Introduction -Definitions, motivation – system models – architectural model – client-server model – peer-to-peer model – distributed computing paradigm - Communication Mechanisms - Communication protocols-RPC- RMI – group communication – external data representation and marshalling – distributed file system – HDFS (7)

DISTRIBUTED PROGRAMMING ALGORITHMS: Fundamental issues & concepts - synchronization – termination detection – clocks – event ordering – locking – snapshots – leader election – replication and coherence – consistency models and protocols – fault tolerance. (5)

TUTORIAL PRACTICE:
1. Basic Master – Worker program and send messages.
2. Write a program to find the summation of largest number in a very larger array of integers. ( The contents of the array should be equally distributed to all processes).
3. Write a parallel program in SPMD to calculate the PI value using integral approximation method.
4. Matrix multiplication, Transpose, using parallel algorithm.
5. Select your own choice of very dense computational problem having divide and conquer method and implement it in parallel algorithm. And produce the performance chart with 2, 4, 6 and 8 nodes.
6. Implement data parallelization algorithm using CUDA and OpenCL
7. Hadoop setup – Map reduce – Programming models – Text mining.

Total: L:45+T:30 = 75

TEXT BOOKS:
1. George Coulouris, Jean Dollimore, Time kindberg and Gordon Blair, “Distributed Systems: Concepts and design”, Pearson, 2021
2. Peter S Pacheco, Matthew Malensec, “An Introduction to Parallel Programming”, Morgan Kaufmann (Elsevier), 2022
3. John L. Hennessy and David A. Patterson, “Computer Architecture a Quantitative Approach”, Elsevier, 2016.

REFERENCES:
1. Andrew S. Tanenbaum and Maarten van Steen, “Distributed Systems, Principles and Paradigm”, Prentice Hall, 2017.
2. Michael J Quinn, “Parallel Computing: Theory and Practice”, Tata Mcgraw-Hill,2017. 3. Michael J Quinn, “Parallel Programming in C with MPI and OpenMP”, McGrawHill, 2017
4. David F. Bacon, Susan L. Graham and Oliver J. Sharp, “Compiler Transformations for High Performance Computing”, Technical report, 1994.

3 2 0 4

HIGH DIMENSIONAL SPACE:Properties of High-Dimensional Space - Gaussians in High Dimension -Bounds on Tail Probability- Applications of the tail bound - Convex analysis (7)

RANDOM PROJECTIONS AND APPLICATIONS IN DIMENSIONALITY REDUCTION:Random Projection and Johnson- Lindenstrauss Theorem - Best-Fit Subspaces and Large-Scale SVD using Random Projections - Spectral Decomposition (6)

OPTIMIZATION FOR LARGE SCALE MACHINE LEARNING:First-order Methods for Large Scale Optimization - Stochastic gradient with sub sampling - Accelerating SGD with preconditioning and adaptive learning - Noise reduction - Dynamic sample size methods - Second order methods- Quasi-Newton methods - Distributed optimization - Derivative free optimization – Contrastive divergence – Markov Chain Monte Carlo – Gibbs sampling (14)

RANDOM MATRICES:Introduction to random matrix theory - Concentration of measure and random matrices - Large Covariance matrix estimation (8)

SPARSE RECOVERY-THEORY AND ALGORITHMS: Introduction to sparse recovery - A Statistical View of Sparse Recovery - Noiseless Sparse Recovery - Approximations - Sampling theorem - Elastic Net - Fussed Lasso - Low-rank Matrix Recovery - Sketching and Randomized Linear Algebra - Approximate nearest neighbour in high dimensional data - Locality sensitive data structures (10)

TUTORIAL PRACTICE: 1. Implementing and evaluating gradient descent for empirical risk minimization on a multinomial logistic regression task on the MNIST dataset.
2. Using different sampling schemes for stochastic gradient descent.(random sampling, sequential sampling, sub sampling)
3. Applications using Distributed ML - Spark MLLib and Graph labs
4. Application using Sparse models
5. Finding neighbours using Locality Sensitive Hashing, min hash techniques

Total L: 45+T:30 = 75

TEXT BOOK:
1. Martin J, Wain Wright, “High-dimensional statistics: A non-asymptotic viewpoint”, Cambridge University Press, 2019
2. T. Tao, “Topics in random matrix theory,” American Mathematical Society, 2012

REFERENCES:
1. Prateek Jain and Purushottam Kar “Non-convex optimization for machine learning”, Now publishers Inc., 2017
2. Greg W, Alice Guionnet, OferZeitouni, “An Introduction to Random Matrices”, Cambridge press, 2010

3 2 0 4

WIRELESS NETWORK OVERVIEW:Wired and wireless Networks- Effect of mobility on systems- Introduction to wireless technologies- RF Overview - Wireless Signal Propagation-Signal-to-Noise Ratio – Modulation - ISM Spectrum - Frequency Hopping Spread Spectrum (FHSS) - Direct Sequence Spread Spectrum (DSSS)- Orthogonal Frequency Division Multiplexing (OFDM) - Coordination mechanisms and MAC protocols for multi-user network access.                                                (8)

WLAN TECHNOLOGIES: IEEE 802.11 System architecture, protocol architecture, 802.11b, 802.11a -WPAN WPA2- WEP - Bluetooth –IEEE 802.15.4Zigbee Wireless data networks- 6LoWPAN -GPRS architecture. (9)

MOBILE NETWORK LAYER:Introduction - Mobile IP: IP packet delivery, Agent discovery, tunneling and encapsulation, IPV6- Network layer in the internet- Mobile IP session initiation protocol - mobile ad-hoc network: Routing: Destination Sequence distance vector, IoT: CoAP, MQTT – DHCP – Mobile TCP(12)

WIRELESS THREATS AND RISKS:Security breaches on wireless Networks- Eavesdropping-Jamming - RF interference -Covert wireless channels-Traffic Analysis Spoofing- DOS attack - Malicious Code -Cryptographic threats- Rogue Access Points - MAC Filtering Attacks -  RADIUS Vulnerabilities – WPA and 802.1x Vulnerabilities -  Attacks on Wireless Gateways (8)

FUTURE TRENDS: Emerging WLAN Related Technologies – 802.11 Trends – Cellular – 802.16 – 802.20 – 802.22 – UWB, Cognitive Radios, RFID – 4G and Data Communications Convergence.4G features and challenges - Applications of 4G – 4G Technologies: Multicarrier Modulation, Smart antenna techniques, IMS Architecture, LTE, Advanced Broadband Wireless Access and Services             (8) (8)

Tutorial Practice:

1. Simulation of a IEEE 802.11 LAN under various conditions using NS-2 simulator.
2. Simulation of a priority MAC protocol using NS-2 simulator.
3. Simulation of different routing protocols using simulators.
4. Simulation of TCP over error-prone wireless network using NS-2 simulator.
5. Development of Mobile application using blue tooth.

Total L: 45+T:30 = 75

TEXT BOOKS:
1. Gary. S. Rogers and John Edwards, “An Introduction to Wireless Technology”, Pearson Education, 2012.
2. KavehPahlavan, Prashant K. Krishnamurthy, “Principles of Wireless Networks : A Unified Approach”, John Wiley, 2011.
3. Vijay.K. Garg , "Wireless Communication and Networking", Morgan Kaufmann Publishers, 2017.

REFERENCES:
1. Feng Zhao and  Leonidas Guibas, “Wireless Sensor Networks-An Information Processing Approach”, Elsevier, 2004.
2. Clint Smith, P.E. and Daniel Collins, “3G Wireless Networks”, Tata McGraw Hill, 2007.
3. Ivan stojmenovic, “Handbook of wireless networks and mobile computing”, John Wiley& Sons, 2006.

23XDAN SURVIVAL ANALYTICS

3 2 0 4

FEATURES OF SURVIVAL DATA: Censoring, Time scale and the origin of time, Basic lifetime functions, Criteria for performing survival analysis (7)

DESCRIPTIVE APPROACHES: KapMeier estimates, Life table models, Group comparison of survival functions (5)

COX PROPORTIONAL HAZARD REGRESSION: The Cox semi-parametric hazard model, Estimation of Cox hazard model with tied survival times; The hazard rate model with time-dependent covariates, Stratified proportional hazard rate model (8)

COUNTING PROCESS AND DIAGNOSTICS OF COX MODEL: The martingale theory- Martingale central limit theorems, Cox- Snell residuals, Schoenfeld residuals, Score residuals, Deviance residuals, Residual analysis on the Cox model (10)

CLASSIFICATION: Classification and regression trees (CART), Chi-Squared automatic interaction detector (CHAID). (10)

CASE STUDIES (5)

Tutorial Practice:

Implement the following problems using R/ Perl Programming
1. Classification Algorithms and Cox Model.
2. Extract information from customer support data.
3. KapMeier estimates, Life table models
4. Residual analysis on the Cox model

Total L: 45+T:30 = 75

TEXT BOOKS:
1. Jolliffe I. T., “Principal Components Analysis (Springer Series in Statistics)”, Springer publications, 2013.
2. Xian Liu, “Survival Analysis: Models and Applications”, John Wiley& Sons, 2013.

REFERENCES:
1. Johannes Ledolter, ”Data Mining and Business Analytics with R”, John Wiley 2013.
2. Dirk F. Moore, Applied Survival Analysis Using R, Springer, 2016

3 2 0 4

INTRODUCTION: Randomized algorithms, generation of random numbers, randomized quick sort, Karger’s min-cut algorithm Las Vegas and Monte Carlo algorithms, computational models and complexity classes. (6)

PROBABILISTIC INEQUALITIES: Union bound, Markov and Chebyshev inequalities-Applications- Occupancy problem, randomized selection- coupon collector’s problem, the Chernoff bound- routing in a parallel computer- a wiring problem. (8)

PROBABILISTIC METHOD: Overview of the method-maximum satisfiability – finding a large cut , Independent Sets (5)

EXPANDER GRAPHSConnectivity in undirected graphs – Expanders and rapidly mixing random walks, Probability amplification of random walks on expanders                               (8)

DATA STRUCTURES AND GRAPH ALGORITHMS: Random Treaps, hashing – hash tables – perfect hashing, skip lists – Fast min-cut. (8)

ONLINE ALGORITHMS: Paging problem-adversary models- paging against an oblivious adversary-relating the adversaries-the adaptive online adversary, k-server problem. (6)

DERANDOMIZATION: The method of Conditional Probabilities – Derandomizing max-cut algorithm – Constructing pair wise independent values modulo a prime – Pair wise independent – large cut. (4)

TUTORIAL PRACTICE:

1. Find solution for s-t min-cut problem adapting min cut algorithm.
2. Problems using treap data structure.
3. Problems using randomized hash table.
4. Comparison of performance ana;ysis of Karger’s min cut with fast min-cut algorithms.
5. Problem using K-server on-line algorithms.
6. Real time application of parallel algortithms for maximum independent set.

Total L: 45+T:30 = 75

TEXT BOOKS:
1.Motwani R and RaghavanP,“Randomized Algorithms”, Cambridge University Press, 2010.
2 Michael Mitzenmacher and Eli Upfal, “Probability & Computing: Randomized Algorithms and Probabilistic Analysis”, Cambridge University Press, 2009.

REFERENCES:
1. Thomas H Cormen, Charles E Leiserson and Ronald L Rivest, “Introduction toAlgorithms”, MIT Press, 2009.
2 James Aspens, “Notes on Randomized algorithms”, Yale University, 2013,

3 2 0 4

REPRESENTATION IN GRAPHICAL MODELS:Kalman Filters, Bayes Ball Theorem and Factorization- Hammersley-Clifford Theorem, Equivalence -I-Maps & Perfect Maps. (8)

FACTOR GRAPHS:Representation, Relation to Bayesian Network and Markov Random Field. (7)

EXACT INFERENCE IN GRAPHICAL MODELS:Relation to Dynamic Programming, Dealing with Evidence, Junction Tree Algorithm, and Belief Propagation - Sum Product. (8)

APPROXIMATE INFERENCE IN GRAPHICAL MODELSVariational Methods -Mean Field, Kikuchi & Bethe Approximation, Expectation Propagation, Gaussian Belief Propagation; MAP Inference - Max-Product, Graph Cuts, Linear Programming Relaxations to MAP -Tree-Reweighted Belief Propagation; Sampling - Markov Chain Monte Carlo, Metropolis Hastings, Gibbs - Collapsing & Blocking, Particle filtering. (12)

LEARNING IN GRAPHICAL MODELS:Expectation Maximization, Maximum Entropy, Pseudo-likelihood, Structure prediction (10)

TUTORIAL PRACTICE:

Implementation of the following algorithms:
1. Loopy Belief Propagation (LBP) (different variant, also for higher-order)
2. Sequential Belief Propagation (BPS) (different variant, also for higher-order)
3. Tree-reweighted Belief Propagation (TRBP) 
4. Gibbs Sampling
5. Adaptive Diminishing Smoothing Algorithm

Total L: 45+T:30 = 75

TEXT BOOKS:
1. Koller, D., and Friedman, N. (2009), Probabilistic Graphical Models: Principles and Techniques. MIT Press.
2. Kevin P. Murphy (2013) Machine Learning: A Probabilistic Perspective. 4th Printing. MIT Press.

REFERENCES:
1. Jensen, F. V. and Nielsen, T. D. (2002). Bayesian Networks and Decision Graphs. Information Science and Statistics. Springer, 2nd edition.
2. Barber, D. (2011). Bayesian Reasoning and Machine Learning. Cambridge University Press, 1st edition.
3. Bishop, C. M. (2011). Pattern Recognition and Machine Learning (Information Science and Statistics). Springer, 2nd printing.

3 2 0 4

TOPOLOGICAL SPACES:Topological Spaces– Basis- Sub basis- Order topology– Product Topology (Finite)– Subspace topology– Metric TopologyClosed sets and limits points – Continuous functions. (8+6)

CONNECTEDNESS AND COMPACTNESS:Connected spaces, connected sub sets of the real line, local connectedness, compact spaces, locally compact spaces (5+3)

COUNTABILITY AND SEPARATION AXIOMS:Countability axioms. separation axioms, normal spaces, Urysohn’s lemma, Tietze extension theorem (6+3)

ALGEBRAIC TOPOLOGY:Homotopy of Paths- The Fundamental Groups- Circle , group of S n , Covering spaces- Retractions of fixed points- The fundamental theorem of Algebra (8+6)

COMPLEXES:Surfaces, Homeomorphisms,,Simplices, Simplicial Complex, Euler characteristics. (8+5)

HOMOLOGY:Simplical Homology, Chain complexes, Cycles and boundaries, Homology groups and Betti numbers, The homology of a ball, Reduced homology, Induced maps, Matrix reduction: Euler-Poincaré formula, Boundary matrices, Smith normal forms, Reduction algorithm; Relative homology groups; Excision, Maps between vector spaces, Exact sequences: Chain complexes and chain maps, The snake or zig-zag, Connecting homomorphism, Mayer-Vietoris sequence, cohomology (10+7)

Total L: 45+T:30 = 75

TEXT BOOK:
1 .Herbert Edlesbrunner and John Harer, ‘Computational Topology – An Introduction’, AMS, 2010.
2. James R. Munkres, ‘Topology- A First Course’, Pearson, 2018.
3. JamesR. Munkres.” Elements of Algebraic Topology’. CRC Press, 2018.

REFERENCES:
1. John M.Lee, ‘Introduction to Topological Manifold, Springer, 2011.
2. G unter Rote and GertVegter.’Computational Topology - An introduction (Effective Computational Geometry for Curves and Surfaces (Chapter 7)), Springer, 2006.

23XDAN RANDOM GRAPHS

3 2 0 4

BASIC MODELS AND EVOLUTION:Revision on Graph theory and probability. uniform random graph, binomial random graph, monotone increasing property, threshold functions, sharp threshold. Pseudo-Graphs.Component structure - Sub-Critical Phase – forests, paths, trees, order of the largest component,cycles. Super-Critical Phase – giantcomponent and trees. Cores. Phase Transition - complex component, largest component, complex largest component. Graph Minors. (10)

VERTEX DEGREES:Degrees of Sparse Random Graphs - maximum (minimum)degree ofvertices. Degrees of Dense Random Graphs – existence of unique vertex of maximum degree, Application to graph isomorphism, edge coloring. (6)

CONNECTIVITY: Wireless Sensor Networks (WSN): Overview, Fault Tolerance - RFID – NFC - Low Power Personal Area networks (LowPAN): Overview, 6LowPAN,IEEE 802.15.4, BLE, Zigbee, Zwave, and Thread - Wi-Fi -Low Power Wide Area Networks (LPWAN): Concepts and features, SigFox, LoraWAN, LPWAN-3GPP,Comparing different LPWAN technologies. (7)

SUBGRAPHS:Density, asymptotic behavior and non-existence of a subgraph, balanced and strictly balanced graphs, threshold for the existence and non-existence, asymptotic distribution and asymptotic behavior. Spanning Subgraphs - Existence of d-dimensional hypercube and Hamilton cycles, Long Paths and Cycles in Sparse Random Graphs, Perfect matching in random graphs. (10)

EXTREME CHARACTERISTICS AND PROPERTIES:Diameter, Largest Independent Sets, Chromatic Number of dense and sparse graphs, eigenvalues.Ramsey and Tuŕan properties. (6)

REAL WORLD NETWORKS:Preferential Attachment Graph, Power Law, Spatial Preferential Attachment model, Preferential Attachment with Deletion, Bootstrap Percolation, Watts-StrogatzModel, Kleinberg’s Model. (6)

PROTOTYPING: Prototyping embedded devices- Open Source versus Closed Source-Embedded Computing Basics-Arduino- Raspberry Pi- Implementation. (2)

APPLICATIONS INIoT: Smart homes – Energy – Health Care – Smart Transportation – Smart Living – Smart Cities- Smart Grid – Smart Agriculture. (2)

Tutorial Practice:

Total L: 45+T:30 = 75

TEXT BOOKS:
1. Alan Frieze and MichalKaronski, Introduction to Random Graphs, Cambridge University Press, 2016.
2. Jonathan Gross, Jay Yellen andMark Anderson, “Graph Theory and its Applications”, Chapman and Hall/CRC Press, 2018.
REFERENCES:
1. RemcoVan Der Hofstad, Random Graphs and Complex Networks, Cambridge University Press, 2016.
2. BelaBollobas, Random Graphs, Cambridge University Press, 2016.
3. Alan Frieze and SvanteJanson, Random Graphs, Wiley, 2017.
4. Albert-LászlóBarabási,MártonPósfai, Network Science, Cambridge University Press, 2016.

23XDO1 COMPUTATIONAL FINANCE

3 2 0 4

SIMPLE MARKET MODEL:Basic notions and Assumptions, No-Arbitrage Principles, one step Binomial model, risk and return, Forward contracts, Call and Put options. (7)

RISK-FREE ASSETS:Time Value of Money- Simple interest , periodic compounding, streams of payments ,continuous compounding, Comparison of compounding methods. Money market- Zero- Coupon bonds, coupon bonds. (7)

PORTFOLIO THEORY -Risk and Return- Expected return, standard deviation as risk measure. Two Securities – Risk and expected return oon a portfolio, feasible set. Several Securities- Risk and Expected return, minimum variance portfolio, efficient frontier , market portfolio. Capital Asset Pricing Model                  (11)

BASIC OPTIONS THEORY – Definitions, Put-call parity. Bounds on option prices- European option, calls on non-dividend paying stock, American options. Variables determining option prices. Binomial models- Single step, two step, several steps, flow of information, filtration. Option pricing- single step, two step, Cox –Ross –Rubinstein formula American claims, martingale property. (12)

CONTINUOUS TIME MODELLimitations of discrete models, Continuous time limit- choice of N-step Binomial model. Black sholes model.  (8)

TUTORIAL PRACTICE:

1. Problems using Capital Asset Pricing model.
2. Problems using Auto correlation.
3. Plot time series data and find outliers
4. Monte Carlo Simulation of options pricing
5. Finding minimum variance portfolio
6. Finding optimal portfolio
7. Implementation Cox-Ross Rubinstein Formula

Total L: 45+T: 30=75

TEXT BOOKS:
1. Capinski M. and Zastawniak T, “Mathematics for Finance: An Introduction to Financial Engineering”, Springer, 2011.
2. Sheldon M. Ross “ An elementary introduction to Mathematical Finance” Cambridge University Press,2011
3. David Ruppert, and David S. Matteson “Statistics and Data Analysis for Financial Engineering”, Springer,2015.
REFERENCES:
1. Simon Benninga, “Financial Modeling”, MIT Press, 2014.
2. Edwin J. Elton, Martin J. Gruber, Stephen J. Brown and William N. Goetzmann “Modern Portfolio Theory and Investment Analysis”, John Wiley, 2014..

3 2 0 4

MATHEMATICAL & GEOMETRICAL REVIEW: analysis – sorting, binary search, balanced binary search, divide and conquer, plane sweep, Kd-trees, Dijkstra’s algorithm, points, lines and planes, basic geometric objects – polygons, polytopes, convexity, graphs - vertex coloring, planar, Euler’s formula.(2)

CONVEX HULLS: Definition, lower bounds, algorithms - Graham's scan, divide and conquer, Jarvis march, 3D hulls.(5)

LINE SEGMENT INTERSECTION: Plane sweep algorithm, Doubly-connected edge list, computing overlay of two subdivisions, Map overlay algorithm, half-plane intersection, arrangements of lines.(8)

POLYGON TRIANGULATION: Art gallery problem – introduction, triangulation, bounds, partition into monotone pieces, triangulating monotone polygon, placement of guards.(8)

ORTHOGONAL RANGE SEARCHING: and 2-D range searching, range trees.(4)

VORONOI DIAGRAMS: , beach line, computing Voronoi diagram, Delaunay triangulations, computing Delaunay triangulations. (8)

ROBOT MOTION PLANNING: Work space and configuration space, point robot, free space, Minkowski sums for convex and nonconvex polygons, translational motion planning, motion planning with rotations, Point location and trapezoidal maps. Visibility graphs - Shortest paths for a point robot, computing visibility graph, shortest paths for a translating polygonal robot.(10)

TUTORIAL PRACTICE:

Implementation of algorithms for the following problems.
1. Convex hull problems.
2. Line and half plane intersections.
3. Map overlay problems using Doubly-connected edge list.
4. Triangulation and Art gallery problem.
5. Orthogonal range searching (1D and 2D) using Kd-trees.
6. Construct Voronoi diagrams.
7. Translational algorithms for robot motion planning.

Total L: 45+T: 30=75

TEXT BOOKS:
1. Mark de Berg, Otfried Cheong, Marc van Kreveld, “Computational Geometry - Algorithms and Applications”, Springer Verlag, 2011.
2. Joseph O’Rourke, “Computational Geometry in C”, Cambridge University Press, 2008.



REFERENCES:
1. Franco P. Preparata and Michael Ian Shamos, “Computational Geometry - An Introduction”, springer, 2011 .
2. Goodman J E and O'Rourke, “Handbook of Discrete and Computational Geometry”, CRC Press, 2004.
3. Subir Kumar Ghosh, “Visibility Algorithms in the Plane”, Cambridge University Press, 2007.

3 2 0 4

PRINCIPLES OF MANAGEMENT: Meaning, Definition and Significance of Management, Basic Functions of Management – Planning, Organizing, Staffing, Directing and Controlling. Organizational Environment – Social, Economic, Technological and Political. Corporate Social Responsibility - Case discussion.(5)

INDUSTRIAL AND BUSINESS ORGANIZATION: Growth of Industries (Small Scale, Medium Scale and Large Scale Industries). Forms of Business Organizations. Resource Management – Internal and External Sources(8)

ORGANIZATIONAL BEHAVIOUR: Significance of OB, Impact of culture on organization. Role of leadership and leadership styles. Personality and Motivational Theories. Attitudes, Values and Perceptions at work - Case discussion(8)

GROUP BEHAVIOUR: Group dynamics, Group formation and development, group structure and group cohesiveness. Informal organization- Conflict MAnagement and Negotiation (8)

GLOBALISATION: Issues for global competitiveness, proactive and reactive forces of globalization. Cross cultural management – Management of work force diversity.(8)

HUMAN RESOURCE MANAGEMENT: Objectives and Functions, Selection and Placement, Training and Development – Conflict management – Stress management - Human resource management in global environment - Human resource information system(HRIS) - Case discussion.(8)

TUTORIAL PRACTICE:

1. Case study on human resource information system.
2. Case study on organizational behavior.
3. Case study on human resource information system.
4. Case study on organizational behavior.

Total L: 45+T: 30=75

TEXT BOOKS:
1. Harold Koontz, Heinz Weihrich and Ramachandra Aryasri, “Principles of Management”, Tata McGraw Hill, 2014.
2. Mamoria C B, “Personnel Management”, Sultan Chand & Sons, 2005.

REFERENCES:
1. John W Newstrom and Keith Davis, “Organizational Behavior”, Tata McGraw Hill, 2010.
2. Stephen P Robbins, ”Organisational behavior”, Prentice Hall, 2010.
3. Errene Leela Rout and Nelson Omiko, “Corporate Conflict Management”, PHI Learning Pvt. Ltd., Delhi, 2014.

3 2 0 4

INTRODUCTION TO ENTREPRENEURSHIP: ENTREPRENEURSHIP:Definition – characteristics and functions of an entrepreneur – common myths about entrepreneurs, importance or entrepreneurship. Creativity and innovation - role of creativity, innovation process, sources of new ideas, methods of generating ideas, creative problem solving, entrepreneurial process. (10)

FORMS OF BUSINESS ORGANIZATION:Sole proprietorship, partnership, limited liability partnership, joint stock companies and cooperatives, starting a small-scale industry. Developing an Effective Business Model - importance and component of an effective business model. (11)

APPRAISAL OF PROJECTS:Importance of evaluating various options and future investments- entrepreneurship incentives and subsidies, appraisal techniques. Financing the new venture - determining financial needs, sources of financing, equity, and debt funding - Evaluating financial performance. (12)

THE MARKETING FUNCTION: Industry Analysis – Competitor Analysis – Marketing Research for the New Venture – Defining the Purpose or Objectives – Gathering Data from Secondary Sources – Gathering Information from Primary Sources – Analyzing and Interpreting the Results – The Marketing Process.(5)

INTELLECTUAL PROPERTY PROTECTION AND ETH ICS: Patents – Copyright - Trademark- Geographical indications – Ethical and social responsibility and challenges.(4)

TUTORIAL PRACTICE:

Case studies

Total L: 45+T: 30=75

TEXT BOOKS:
1. Donald F.Kuratko and Richard M.Hodgetts, “Entrepreneurship”, South-Western, 2003.
2. Vasant Desai,“The Dynamics of Entrepreneurial Development and Management”, Himalaya Publishing House, 2010.


REFERENCES:
1. S.L.Gupta, Arun Mittal, “Entrepreneurship Development”, International Book House, 2012.
2. G. S. Sudha, “Management and Entrepreneurship Development”, Indus Valley Publication, 2009.
3. V. Badi, N. V. Badi ,“Business Ethics”, Vrinda Publication (P) Ltd.,, 2012.
4. Prasanna Chandra “Projects- Planning, Analysis, Financing, Implementation andreview”, TATA McGraw Hill, 2012.

3 2 0 4

INFORMATION THEORY:Introduction - Measure of information - Information content of message - Average Information content of symbols in Long Independent sequences - Average Information content  of symbols in Long dependent sequences - Markov statistical Model for Information Sources - Entropy and Information rate of Mark off sources (8)

SOURCE CODING:Encoding of the Source Output - Shannon's Encoding Algorithm - Shannon Fano Encoding Algorithm - Source coding theorem - Prefix Codes - Kraft McMillan Inequality property KMI - Huffinan codes.                                             (8)

INFORMATION CHANNELS:Communication Channels - Discrete Communication channels – Channel Matrix - Joint probabilty Matrix, Binary Symmetric Channel - System Entropies. Mutual Information - Channel Capacity - Channel Capacity of Binary Symmetric Channel. Binary Erasure Channel - Muroga's Theorem - Differential entropy.                                                           (9)

ERROR CONTROL CODING –Methods of Controlling Errors - Types of Errors - types of Codes - Linear Block Codes: matrix description of Linear Block Codes - Error detection & Correction capabilities of Linear Block Codes - Single error correction Hamming code - Table lookup Decoding using Standard Array.

BINARY CYCLIC CODES:Algebraic Structure of Cyclic Codes - Encoding using an (n-k) Bit Shift register -Syndrome Calculation -Error Detection and Correction - Convolution Codes: Convolution Encoder -Time domain approach - Transform domain approach - Code Tree -Trellis and State Diagram -The Viterbi Algorithm.                                                        (10)

Total L : 45 + T :30 =75 hours 

Tutorial  Practice :  
1. Determination of entropy of a given source.
2. Determination of various entropies and mutual information of a given channel.
 a) Noise free channel.
 b) Error free channel
 c) Binary symmetric channel
 d) Noisy channel
 Compare channel capacity of above channels.
3. Generation and evaluation of variable length source coding.
 a) Shannon – Fanocoding and decoding
 b) Huffman Coding and decoding
4. Coding & decoding of Linear block codes.
5. Coding & decoding of Cyclic codes.
6. Coding and decoding of convolutional codes.
7. Coding and decoding of BCH and RS codes.
8. Performance of a coded and uncoded communication system (Calculate the error probability).

TEXT BOOKS:
1. Reza F M, “An Introduction to Information Theory”, Dover Publications, 2012
2. Joy A Thomas, Cover M, “Elements of Information Theory”, John Wiley, 2007.
3. T. D. Moon, “Error Correction Coding: Mathematical Methods and Algorithms”, Wiley, 2005

REFERENCES:
1. Ranjan Bose, “Information Theory, Coding and Cryptography”, ‎ McGraw Hill Education, 2017.
2. HariBhat, Ganesh Rao, “Information Theory and Coding”, Cengage, 2017.
3. Sam Shanmugam, “Digital andAnalog Communication Systems”,  John Wtley, 2006.
4. Simon Haykin, “Digital Communication”, John Wiley, 2008
5. S. Lin & D. J. Costello, Error Control Coding,  Prentice-Hall, 2004.

3 2 0 4

COST ACCOUNTING: Cost classification - significance of overhead Cost - Preparation of Cost sheet - Concept of cost volume profit analysis - Concept of variance - Principles of Job Costing, batch costing and Process costing - Operating Costing - Modern techniques/concepts of Cost Control/ Cost Management. (10)

FINANCIAL ACCOUNTING: Double Entry Book keeping concepts - Journalisation of Business Transactions - Subsidiary Books - Preparation of Profit and Loss Account and Balance sheet from Trial balance - Simple problems - Methods of depreciation. (10)

FINANCIAL RATIO ANALYSIS: Uses and Nature - preparation of Liquidity Ratios - coverage Ratios and profitability Ratios from profit & Loss Account and Balance sheet - common size Income statement and common size Balance sheet. (10)

GOALS AND FUNCTIONS OF FINANCIAL MANAGEMENT: Finance function - Importance of Corporation finance - objectives of Financial Management - organization of the finance function - concept of time value of money. (5)

PRINCIPLES OF CAPTIAL BUDGETING: Kinds of capital Budgeting Decisions - Evaluation of proposals from the given cash inflows - Net present value versus Internal rate of return method problems. (5)

WORKING CAPITAL MANAGEMENT: Definition and importance of working capital - factors affecting working capital - Inventory management - simple problems - Receivables Management - cash Budget Preparation - Estimate of overall working capital requirements - Various sources of financing . (5)

TUTORIAL PRACTICE:
Case studies

Total L:45+T:30=75

TEXT BOOKS :
1. Khan M Y, Jain P K, "Cost Accounting and Financial Management", Tata McGraw Hill, 2008.
2. Gupta R L, Radhaswamy M, "Advanced Accountancy", Sultan Chand & Sons, 2009.

REFERENCES :
1. Sharma R K and Shashi K Gupta, "Management Accounting - Principles and Practice", Kalyani Publishers, 2011.
2. Kuchal S C, "Financial Management", Chaitanya Publishing House, 2006.

3 2 0 4

NATURAL RESOURCES, ECOSYSTEMS AND BIODIVERSITY: Environment, Definition, Scope and importance, Forest resources, Use and overexploitation, Water resources: Use and over utilization. Eco system ; Structure and functions of an eco-system, energy flow in the eco system. Bio Diversity; values of biodiversity, biodiversity at global, national and local levels – threats to bio diversity. Conservation of bio diversity – In-situ & Ex-situ conservation. (9)

ENERGY SOURCES: Growing energy needs, Renewable and non-renewable energy sources, Hydro power, Solar Power: Photovoltaic Energy – Motivation for going Solar – Solar Electricity – PV cells. Wind Power: – Using the Wind: Generating Power at Remote Sites,– Measuring the Wind – Estimating the output. Use of alternate energy sources. (9)

SOCIAL ISSUES AND THE ENVIRONMENT: From unsustainable to sustainable development, Urban problems related to energy, Water conservation, Rain water harvesting, Watershed management, Environment and human health, Role of information technology in environment and human health. Environment Protection Act: Air (Prevention and Control of Pollution) Act – Water Act, Forest Conservation Act, Wildlife Protection Act, Introduction to EIA and ISO 14000. (9)

ENVIRONMENTAL POLLUTION AND DISASTER MANAGEMENT: Definition – causes, effects and control measures of air pollution, water pollution, soil pollution, noise pollution, thermal pollution and nuclear hazards. Disaster management - floods, earthquake, cyclone and landslides. Solid waste management - causes, effects and control measures of municipal solid wastes (Biomedical wastes, hazardous wastes). Role of an individual in prevention of pollution. (9)

GLOBAL ATMOSPHERIC CHANGE & GREEN FUNDAMENTALS: The Atmosphere of Earth – Global Temperature – Global Energy Balance, The Greenhouse Effect - Environmental Issues and Green Computing, Electronic waste management: Introduction;- Environment and society, producer responsibility legislation – the Waste Electrical and Electronic Equipment (WEEE) directive, Materials Composition of WEEE: Mobile Phones – Television – Washing Machines, - Current and new electronic waste recycling technology- Future perspectives of electronic scrap. (9)

TUTORIAL PRACTICE:
Case Studies

Total L:45+T:30=75

TEXT BOOKS:
1. Mackenzie L. Davis, and David A. Cornwell, “Introduction to Environmental Engineering”, Tata McGraw Hill, 2010
2. Chetan Singh Solanki, “Solar Photovoltaics”, PHI Learning Private Ltd., 2011.
3. Siraj Ahmed, “Wind Energy: Theory and Practice”, PHI Learning Private Ltd., 2011.

REFERENCES
1. William W. Nazarodd and Lisa Alvarez-Cohen, “Environmental Engineering Science”, Wiley-India, 2010
2. Anubha Kaushik and Kaushik C P, “Environmental Science and Engineering”, New Age International Pvt Ltd, 2005.
3. Martha Maeda, “How to Solar Power your Home”, Atlantic Publishing Group, 2011.
4. Paul Gipe, “Wind Power – Renewable Energy for Home, Farm and Business”, Sterling Hill Publications, 2008.
5. Klaus Hieronymi, Ramzy Kahhat, Eric Williams, “E-Waste Management : From Waste to resource”, Routledge – Taylor and Fransis, 2012.

3 2 0 4

METRIC SPACES: Definition, examples, open and closed sets, neighborhood, compact sets, convergence, Cauchy sequence, completeness, completion of metric spaces. (12+8)

BANACH SPACE: Normed spaces, Banach space - Definition, examples, properties, finite dimensional normed spaces and subspaces, compactness and finite dimension. (8+6)

LINEAR OPERATORS: Definition and examples, bounded and continuous linear operators, linear functional on finite dimensional spaces, normed spaces of operators, Hahn Banach theorem, open mapping theorem, closed graph theorem (12+8)

HILBERT SPACE: Inner product space, Hilbert space –Definition and examples, properties of inner product space, orthogonal complements, orthogonal sets and sequence, orthonormal sets and sequence, series corresponding to orthonormal sequences Bessel’s inequality. (13+8)

Total L: 45+T:30 = 75

TEXT BOOKS:
1. Erwin Kreyszig, “Introductory Function Analysis with Applications”, John Wiley, 2007.
2. George F Simmons, “Introduction to Topology and Modern Analysis”, Tata Mc-Graw Hill, 2017.

REFERENCES:
1. Limaye B.V. “An Introduction to Functional Analysis”, Newage International, 2014.

3 2 0 4

FORMULATION COMPLEXITY AND RELAXATIONS:Introduction-mixed integer programs and applications-formulations- good and ideal formulations(5)

OPTIMALITY RELAXATION AND BOUNDSoptimality and relaxations-Linear programming relaxation-combinatorial relaxation-linearization and modelling methods- algorithms for relaxation(8)

INTEGER LINEAR PROGRAMMING AND GOAL PROGRAMMING: ::Binary integer programming model- Innovative use of binary variable in model formulation-solving integer and mixed integer linear programming-Mixed integer optimality conditions- Constrained programming, Applications ,Goal Programming Formulation; Goal Programming Algorithms; The Weights Method; The Preemptive Method

MULTI OBJECTIVE OPTIMIZATION:Dominant set, Pareto optimality, multi criteria decision making-examples. (8)

ENUMERATIVE AND HEURISTIC METHODS :Branch and bound-optimization based heuristic methods- Deterministic dynamic programming-approximate dynamic programming- Applications of dynamic programming Basics to meta heuristics optimization –Simulated Annealing-Tabu Search- Genetic algorithms, multi objective genetic algorithm- Ant systems (8)

APPROXIMATION ALGORITHMS:Primal dual methods-randomized rounding of linear relaxation-randomized rounding of convex relaxation-approximation schemes-limitation in approximability.(7)

Tutorial Practice:

Case studies

Total L: 45+T:30 = 75

TEXT BOOKS:
1. Frederick S. Hillier, Gerald J. Lieberman, ”Introduction to Operations Research”, McGraw-Hill Higher Education, 2010.
2. David R. Anderson, Dennis J. Sweeney, Thomas A. Williams, Jeffrey D. Camm, R. Kipp Martin, “An Introduction to Management Science: Quantitative Approaches to Decision Making, Cengage Learning , 2011
3. Bertsimas and R. Weismantel, Optimization over Integers, Dynamic Ideas, 2005
REFERENCES:
1. Stephen P. Boyd, Lieven Vandenberg he, “Convex Optimization”, Cambridge University Press, 2004.
2. Carlos A Coello Coello, David A Van Veldhuizen, Gary B. Lamont, “Evolutionary Algorithms for solving multi objective problems”, Springer Science Business media, 2002.

3 2 0 4

INTRODUCTION and IoT ARCHITECTURE: Introduction to Internet of Things (IoT) – Machine to Machine (M2M) – Features and Definition of IoT– Recent Trends in the Adoption of IoT- IoT ARCHITECTURE: Functional Requirements - IoT Enabling Technologies –Basic Architecture Components of IoT: Embedded Computation Units, Microcontrollers, System on Chip (SoCs) - Sensors – Actuators – Communication Interfaces (8)

IoT DATA COMMUNICATION: IPv6 - Sensor Networks- RFID – NFC - Low Power Personal Area networks (Low PAN): Overview, 6LowPAN - IEEE 802.15.4, BLE, Zigbee, -LPWAN: Concepts and features, SigFox, LoRaWAN, LPWAN-3GPP, - SDN for IoT - Comparing different LPWAN technologies (10)

APPLICATION LAYER PROTOCOLS IN IoT: CoAP: Architecture- Features, Applications - MQTT: Architecture, Feature, Applications - AMQP – Rabbit MQ -Comparing different IoT Application Layer Protocols- design of web application using RESTful web API (6)

IoT DATA MANAGEMENT: Cloud platforms for IoT - Sensor cloud – Fog /Edge Computing - Data Storage and retrieval – database connectivity with IoT devices- MySQL, NoSQL, NewSQL- Data Analysis using IPython – Visualization and interpretation of data (10)

PROTOTYPING and APPLICATIONS: rototyping embedded devices - Open source vs Closed source – Arduino and Raspberry Pi Implementation - Smart Homes - Energy - Health care - Smart transportation - Smart grid - Smart cities - Smart Agriculture (4)

SECURITY IN IoT ENABLED DEVICES: Security attacks in IoT- malware propagation and control in IoT- privacy preservation – Authentication in IoT – Authorization in IoT- OAuth 2.0 in IoT - Blockchain in IoT security (7)

TUTORIAL PRACTICE:

1. Smart Home automation using Raspberry PI and Arduino
2. IoT cloud platforms
3. ThingSpeak API and MQTT
4. Interfacing ESP 8266 with Web services
5. Connected Vehicle applications
6. IoT Based Fall Detection system
7. Smart Energy Meter Monitoring
8. IoT Based safety alarm System
9. IoT security applications

Total L: 45 + T :30=75

TEXT BOOKS:

1. Milan Milenkovic, ”Internet of Things: Concepts and system design”, Springer, 2021
2. Mayur Ramgir, “Internet of Things: Architecture, Implementation and Security”, Pearson, 2019
3. Vijay Madisetti ans Arshdeep Bagha,” Internet of Things( A hands on Approach)”, VPT, 2014
4. Honbo Zhou,” The internet of tings in the Cloud: A middleware perspective”, CRC Press, 2012

REFERENCES:

1. Simone Cirani, Gianluigi Ferrari, Internet of Things, Architectures, Protocols and Standards, John Wiley, 2019
2. Mohammad Ilyas, The Handbook of Ad Hoc Wireless Networks, CRC Press, 2017
3. Dieter Uckelmann, Mark Harrison, Florian Michahelles, “Architecting the Internet of Things”, Springer, 2011
4. Adrian McEwen, Hakim Cassimally, “Designing the Internet of Things”, John Wiley, 2014

3 2 0 4

MOBILE APPLICATION DEVELOPMENT: Introduction to Android Platform – Android and MVC architecture - Application life cycle - UI design for Android - UI fragments - Different types of layouts – Widgets – List view – Fragment navigation – Dialogs - Intents (14)

INTRODUCTION: Introduction to mobile applications - Importance of mobile applications – Strategies and challenges – Software and hardware requirements for developing mobile applications – Types of mobile applications – Benefits of creating mobile applications – Marketing and advertising mobile applications – Mobile devices overview and classification. (3)

MOBILE USER INTERFACE DESIGN: Mobile application users – Social aspect of mobile interfaces - Accessibility – Design patterns – Designing for the various mobile platform - Adaptive Mobile Websites – Dedicated Mobile Websites. (4)

MOBILE APPLICATION DEVELOPMENT: Introduction to Android Platform – Android and MVC architecture - Application life cycle - UI design for Android - UI fragments - Different types of layouts – Widgets – List view – Fragment navigation – Dialogs - Intents (14)

DATABASE: Creating Database – Room architecture – Accessing database – Querying database. (3)

ADVANCED FEATURES: Using Resources and Media – Audio playback – Retained Fragments – Creating options menu - Ancestral navigation – Saving and loading data to local files – Android file system – Creating and implementing context menu - Image handling – Background Services – Tracking device location – Jetpack compose – UI and Layouts. (14)

ANDROID SECURITY MODEL: Android architecture – Dalvik VM – Permissions – User Management – Device Security. (7)

TUTORIAL PRACTICE:

TUTORIAL PRACTICE:

1. Android SDK installation and study
2. Defining Layouts
3. Single Activity Application, Application with multiple activities, using intents to Launch Activities
4. Application using GUI Widgets
5. Application with Notifications
6. Application using resources and media
7. Application studying background services
8. Application tracking mobile devices
9. Creating and Saving Shared Preferences and Retrieving Shared Preferences
10. Usage of SQLite Databases for storage
11. Working with Retrofit library in Android Applications
12. Android Automated Testing Frameworks
13. Study of Android Jetpack components
14. Case Study: Dagger Framework for Android
15. Case Study: Cross platform applications

Total L: 45 + T:30 = 60

TEXT BOOKS:

1. Chris Stewart, Bryan Sills, Kristin Marsicano, and Brian Gardner, “Android Programming: The Big Nerd Ranch Guide”, Addison Wesley, 2022.
2. Jeff McWherter and Scott Gowell, “Professional Mobile Application Development”, John Wiley, 2012.
3. Nikolay Elenkov, “Android Security Internals: An in-depth guide to Android’s Security Architecture”, No Starch Press, 2014.

REFERENCES:

1. Ronan Schwarz, Phil Dutson, James Steele, and Nelson To, “The Android Developer's Cookbook - Building Applications with the Android SDK”, Addison Wesley, 2014.
2. Mark Murphy, “The Busy Coder's Guide to Android Development”, Commons Ware, 2019.

3 2 0 4

INTRODUCTION TO AR & VR: Categorizing the realities – Virtual Reality, Augmented Reality & Mixed Reality, Introduction, features and application areas of Virtual Reality, Augmented Reality & Mixed Reality. (6)

VR SDK’s – VR SDK’S and Frameworks: VR Concept Integration- Motion Tracking, Controllers, Camera , Hardware and Software requirements, Mobile VR Controller Tracking, Object Manipulation, Text optimizing and UI for VR. (8)

AR FOUNDATION: Detection of surfaces, identifying feature points, track virtual objects in the real world, face and object tracking. AR Algorithms – Briefing on SLAM Algorithm (Simultaneous Localization and Mapping), understanding uncertain spatial relationship, Anatomy of SLAM, Loop detection and Loop closing Unity AR concepts- Pose tracking, Environmental detection, Raycasting and physics for AR, Light estimation, Occlusion, working with ARCore and ARKit. (10)

VR DEVICES: Structure and working of VR Devices. AR Components – Scene Generator, Tracking system, monitoring system, display, Game scene AR Devices – Optical See-Through HMD, Virtual retinal systems, Monitor based systems, Projection displays, Video see-through systems. Advantages and Disadvantages of AR and VR technologies. (8)

TRENDING APPLICATION AREAS: Gaming and Entertainment, Architecture and Construction, Science and Engineering, Health and Medicine, Aerospace and Defence, Education, Telerobotics and Telepresence (9)

HUMAN FACTORS, LEGAL AND SOCIAL CONSIDERATIONS: Human Factors Considerations, Legal and Social Considerations, The Future(4)

TUTORIAL PRACTICE:

1. Develop a scene that includes a cube, plane, and sphere, apply transformations on the objects.
2. Add video and audio source.
3. Create a new material and texture separately. Change color, material, and texture of each object separately.
4. Create a scene that includes a sphere and plane. Apply rigid body component, material, and Box collider to the objects.
5. Develop a simple UI menu with images, canvas, sprites, and button. Interact with the UI menu through VI trigger button such that on each successful trigger interaction display score on a scene.
6. Create an immersive environment (living room / battle field / tennis court) with only static game objects. 3D game objects could be created using 3D design tools.
7. Include animation and interaction in the immersive environment.
8. Create VR environment for any use case. This application can include at least 4 scenes which could be changed dynamically. (VR application to visit a Zoo).
9. Create AR environment for online furniture sales.
10. Create a multiplayer game using VR / AR.

Total L: 45 + T: 30 = 75

TEXT BOOKS:

1. Dieter Schmalstieg, Tobias Hollerer, “Augmented Reality: Principles and Practice”, Pearson Education, 2016.
2. William R. Sherman, Alan B. Craig, “Understanding Virtual Reality: Interface, Application, and Design”, Morgan Kaufmann Publishers, 2018.

REFERENCES:

1. Steve Aukstakalnis, “Practical Augmented Reality: A Guide to the Technologies, Applications, and Human Factors for AR and VR”, Addison-Wesley Professional, 2016.
2. Gerard Kim, “Designing Virtual Reality Systems: The Structured Approach”, Springer, 2009.
3. Alan B. Craig, William R. Sherman, Jeffrey D. Will, “Developing Virtual Reality Applications”, Morgan Kaufmann, 2009.

3 2 0 4

INTRODUCTION TO HACKING: Introduction to Hacking – Importance of Security – Elements of Security – Phases of an Attack –Types of Hacker Attacks – Hacktivism – Vulnerability Research – Introduction to Footprinting – Information Gathering Methodology –Footprinting Tools – WHOIS Tools – DNS Information Tools – Locating the Network Range – Meta Search Engines. (10)

SCANNING AND ENUMERATION: Introduction to Scanning – Objectives – Scanning Methodology – Tools – Introduction to Enumeration – Enumeration Techniques – Enumeration Procedure – Tools. (7)

SYSTEM HACKING: Introduction – Cracking Passwords – Password Cracking Websites – Password Guessing – Password Cracking Tools – Password Cracking Countermeasures – Escalating Privileges – Executing Applications – Keyloggers and Spyware. (9)

VULNERABILITY ANALYSIS: Vulnerability Assessment Concept - Life-Cycle - Vulnerability Assessment Solutions – Vulnerability Scoring Systems - Vulnerability Scanning - Nessus Tool - Windows OS Vulnerabilities – Tools for Identifying Vulnerabilities – Countermeasures – Linux OS Vulnerabilities – Tools for Identifying Vulnerabilities – Countermeasures. (10)

SOCIAL ENGINEERING AND DENIAL-OF-SERVICES: Social Engineering – Concepts - Phases of a Social Engineering Attack - Types of Social Engineering- Insider Attack- The process of Identity theft- Social Engineering Countermeasures – DoS/DDoS – Attack Techniques - Basic Categories - Botnets - Other DDoS Attack tools - countermeasure Strategies (9)

TUTORIAL PRACTICE:

1. Demonstration of ARP, IP and DNS Spoofing attacks using open-source tools like Arpspoof, Synner, and dns-spoof.
2. Exploration of vulnerabilities in the TCP / IP protocols using open-source testbed:
   • ARP cache poisoning,
   • SYN flooding attack,
   • TCP RST attack and
   • TCP session hijacking attack.
3. Information Gathering about target system using Nmap, DNSRecon, recon-ng, Nikto, and Malgeto.
4. Scanning the target host to find the vulnerabilities using Nessus, OpenVAS, Burp suite and Retina.
5. Demonstration of signature-based IDS with Snort and Suricata.
6. Vulnerability Exploitation and gaining access into the target host using Metasploit, Armitage, SQLmap, and Social Engineering Toolkit.
7. Maintaining the access with target system with Netcat, Meterpreter, backdoor Factory.

Total L: 45+T:30 = 75

TEXT BOOKS:

1. Ec-Council, “Ethical Hacking and Countermeasures: Attack Phases”, Delmar Cengage Learning, 2009
2. Michael T. Simpson, Kent Backman, James E. Corley, “Hands-On Ethical Hacking and Network Defense”, Cengage Learning, 2012.

REFERENCES:

1. Patrick Engebretson, “The Basics of Hacking and Penetration Testing – Ethical Hacking and Penetration Testing Made Easy”, Syngress Media, 2013.
2. Jon Erickson, “Hacking: The Art of Exploitation”, No Starch Press, 2008.
3. RafayBaloch, “Ethical Hacking And Penetration Testing Guide”, CRC Press, Taylor & Francis Group,2015

3 2 0 4

COMPUTER AND FORENSICS: Introduction – Stand-alone computer crimes –Computer evidence – Computer Forensics evidence and courts –Internet laws and statutes; Forensics process – Securing evidence – Law enforcement and methodology. (8)

FORENSICS EVIDENCE: Sources – Seizure – Collection – Integrity – Handling; Acquisition and Duplication of data. (8)

DATA ANALYSIS: Metadata extraction – File Signature analysis – System analysis – Examining unallocated space – Data carving – Recovering deleted data and partitions. (6)

WINDOWS FORENSICS: Registry Analysis – Executable file analysis – Recycle Bin Forensics – Evidence Recovery from Print and Spool files. (5)

INTERNET FORENSICS: Domain Name Ownership Investigation – Email Forensics – Messenger Forensics – Browser Forensics. (6)

MOBILE DEVICE FORENSICS: Hand-held devices and Forensics – Reconstructing user’s activities and deleted data. (4)

MEMORY FORENSICS AND MALWARE ANALYSIS: Memory data collection and Examination – Analyzing Windows and Linux systems for malware – Reverse Engineering tools and techniques. (6)

ANTI-FORENSICS: Erasing Evidence. (2)

TUTORIAL PRACTICE:

1. Implementation of data analysis techniques.
2. Implementation of system analysis concepts.
3. Implementation of email forensics concepts.
4. Implementation of hand-held device forensics activities.

Total L: 45+T:30 = 75

TEXTBOOKS:

1. Marjie T. Britz, "Computer Forensics and Cyber Crime: An Introduction", Pearson Education, 2013.
2. Linda Volonino, Reynaldo Anzaldua, Jana Godwin, "Computer Forensics: Principles and Practices", Pearson/Prentice Hall, 2007.

REFERENCES:

1. Chuck Easttom, "System Forensics, Investigation, and Response", Jones & Bartlett Publishers, 2014.
2. SatishBommisetty, RohitTamma, Heather Mahalik, "Practical Mobile Forensics", Packt Publishing Ltd, 2014.
3. Robert Jones, "Internet Forensics", O'Reilly Media, 2005.

3 2 0 4

INTRODUCTION: Randomized and approximation algorithms-motivation and examples. (3)

RANDOMIZED ALGORITHMS: Random numbers generation- Las Vegas and Monte Carlo algorithms- randomized quick sort -Karger’s min-cut algorithm-occupancy problem - coupon collector’s problem- max-SAT problem and Markov chain analysis. (10)

CHERNOFF BOUND: Derivation-load balancing-hyper cube routing (6)

RANDOMIZED DATA STRUCTURES: Random Treaps- hashing– perfect hashing, skip lists (4)

APPROXIMATION ALGORITHMS: Introduction- vertex-cover – set cover-metric TSP- multiway cut- minimum make span scheduling-FPTAS, PTAS, FPTAS for knapsack. (9)

LINEAR PROGRAMMING RELAXATIONS: Basic properties of linear programming-deterministic rounding-vertex cover-half integrality of vertex cover-randomized rounding-set cover. (7)

PRIMAL-DUAL ALGORITHMS: LP-duality, min-max relations and LP duality - primal dual method for weighted vertex cover, multiway cut, sum multi-commodity flow. (6)

TUTORIAL PRACTICE:

1. Pseudo-random number generators – Blum Blum Shub and linear congruential number generators
2. Find a solution for s-t min-cut problem adapting min-cut algorithm.
3. Problems using treap data structure.
4. Problems using randomized hash table.
5. Problem related to vertex-cover algorithm.
6. Problems related to minimum makespan scheduling.
7. Solving problems using Greedy algorithm for makespan.
8. Problems related to Euclidean TSP.

Total L: 45+T: 30=75

TEXTBOOKS:

1. Motwani R, Raghavan P, “Randomized Algorithms”, Cambridge University Press, 2014.
2. David P. Williamson, David B. Shmoys, “The design of approximation algorithms”, Cambridge University Press, 2011
3. Vijay V. Vazirani, “Approximation Algorithms”, Springer, 2013.

REFERENCES:

1. Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, Clifford Stein, “Introduction to Algorithms”, MIT Press, 2022.
2. Michael Mitzenmacher, Eli Upfal, “Probability & Computing: Randomized Algorithms and Probabilistic Analysis”, Cambridge University Press, 2017.