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COURSE DESCRIPTIONS

E0 219 (3:1) Linear Algebra and Applications Dilip Patil / R. Vittal Rao

Vector Spaces : Subspaces, Linear independence, Basis and dimension, orthogonality. Matrices : Solutionsof linear equations, Gaussian elimination, Determinants, Eigenvalues and Eigenvectors, Characteristicpolynomial, Minimal polynomial, Positive definite matrices and Canonical forms. Singular ValueDecomposition, Applications.

References:

G Strang, Linear Algebra and Applications, Thomson-Brooks/Cole, 4th edition, 2006.

E0 220 (3:1) Graph Theory and Combinatorics L. Sunil Chandran

(1)Graph Theory: Connectivity, Matchings, Hamiltonian Cycles, Coloring Problems; Network flows, specialclasses of graphs. Introduction to Graph Minor theory. (2)Combinatorics: Basic Combinatorial Numbers,Recurrence Relations, Inclusion- Exclusion Principle, Introduction to Polya Theory. (3)Probabilistic Methodin Graph theory: Basic Method, Expectation, Chernoff bound, Lovasz Local Lemma.

References:

J. H. Van Lint, R. M. Wilson, A Course in Combinatorics, Cambridge University Press, 1993N. Alon and J. Spenser, "Probabilistic Methods", John Wiley and Sons, 2nd edition, 2000R. Diestel, "Graph Theory", Springer-Verlag, 2nd edition, 2000

E0 221 (3:1) Discrete StructuresAmbedkar Dukkipati / Dilip

P. Patil / L. Sunil Chandran

Basic mathematical notions: Logic, sets, equivalence relations, functions, axiom of choice; Abstract orders:Quasi-orders, partial orders, Zorn’s lemma, lattices, Boolean algebra, well orders, K¨onig’s theorem;Combinatorics: Pigeonhole principle, The principle of inclusion and exclusion, recurrence relations;Elementary number theory: Peano axioms, mathematical induction, prime numbers, integers, fundamentaltheorem of arithmetic; Groups: Isomorphism theorems, Sylow theorems, Group actions, Polya’s theoryRings and Fields: Ideals, polynomial rings, Chinese remainder theorem, finite fields; Graph theory:representation of graphs, Hamilton paths and cycles, trees.

References:

Laszlo Lovasz, Jozsef Pelikan, Katalin L. Vesztergombi: Discrete Mathematics, Springer 2003.Graham,R.L., Knuth, D.E. and Patashnik, O: Concrete Mathematics: A Foundation for ComputerScience, Addison-Wesley Professional; 2 edition, 1994.Herstein I N : Topics in Algebra, 2 ed., Wiley India 1975.

E0 222 (3:1) Automata Theory and Computability Deepak D Souza / priti

Finite-state automata, including the Myhill-Nerode theorem, ultimate periodicity, and Buchi's logicalcharacterization. Pushdown automata and Context-free languages, including deterministic PDA's, Parikh'stheorem, and the Chomsky-Shutzenberger theorem. Turing machines and undecidability, including Rice'stheorem and Godel's incompleteness theorem.

References:

Hopcroft J.E. and Ullman J.D.: Introduction to Automata, Languages and Computation. AddisonWesley, 1979.Dexter Kozen: Automata and Computability. Springer 1999.Wolfgang Thomas: Automata on infinite objects, in Handbook of Theoretical Computer Science,Volume B, Elsevier, 1990.

Computer Science and Automation - IISc http://www.csa.iisc.ernet.in/academics/academics-courses-desc.php#E0232

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E0 223 (3:1) Automated VerificationAditya Kanade / Deepak D

Souza

(1) Formal models of systems: labelled state transition diagrams for concurrent processes and protocols,timed and hybrid automata for embedded and real-time systems. (2) Specification logics: propositional andfirst-order logic; temporal logics (CTL, LTL, CTL*); fixpoint logic: mu-calculus. (3) Algorithmic analysis:model checking, data structures and algorithms for symbolic model checking, decision procedures forsatisfiability and satisfiability modulo theories. (4) Tools: Student projects and assignments involving modelchecking and satisfiability tools e.g. zChaff, SPIN, NuSMV, Uppaal.

References:

Michael Huth, Mark Ryan: Logic in Computer Science: Modelling and Reasoning about Systems,Cambridge University Press, 2004.Edmund M. Clarke, Orna Grumberg, Doron Peled: Model Checking, MIT Press, 2001.Daniel Kroening, Ofer Strichman: Decision Procedures: An Algorithmic Point of View, Springer,2008.

Prerequisites

Basics of data structures, algorithms, and automata theory.

E0 225 (3:1) Design and Analysis of Algorithms Sathish Govindarajan

Review of basic data structures, searching, sorting. Algorithmic paradigms, e.g., greedy algorithms, divideand conquer strategies, dynamic programming. Advanced data structures. Graph algorithms. Geometricalgorithms, Randomized algorithms. NP and NP-completeness.

References:

Jon Kleinberg and Éva Tardos, Algorithm Design, Addison Wesley, 2005.Cormen, T.H., Leiserson, C.E., Rivest, R.L. and Stein C, Introduction to Algorithms, 2nd Edition,Prentice Hall, 2001.Aho, A.V., Hopcraft J.E., and Ullman, J.D., Design and Analysis of Algorithms, Addison-Wesley,1974.

E0 226 (3:1) Linear Algebra and ProbabilityDilip P. Patil / Ambedkar

Dukkipati

Linear Algebra: System of Linear Equations, Vector Spaces, Linear Transformations, Matrices, Polynomials,Determinants, Elementary Canonical Forms, Inner Product Spaces, Orthogonality. Probability: ProbabilitySpaces, Random Variables, Expectation and Moment generating functions, Inequalities, Some SpecialDistributions. Limits of sequence of random variables, Introduction to Statistics, Hypothesis testing.

References:

Gilbert Strang, Linear Algebra and its Applications, Thomson-Brooks/ Cole, 4th edition, 2006.Hoffman and Kunze, Linear Algebra, Prentice Hall, 2nd edition, 1971.Kishor S. Trivedi, Probability and Statistics with Reliability, Queueing, and Computer ScienceApplications, Wiley, 2nd edition, 2008.Vijay K. Rohatgi, A. K. Md. Ehsanes Saleh, An Introduction to Probability and Statistics, Wiley, 2ndedition, 2000.Kai Lai Chung, Farid Aitsahlia, Elementary Probability Theory, Springer, 4th edition, 2006.

E0 227 (3:1) Program Analysis and VerificationK.V. Raghavan / Deepak D

Souza

Semantics of programs: denotational semantics, operational semantics, Hoare logic. Dataflow analysis:Computing join-over-all-paths information as the least solution to a set of equations that model theprogram statements, analysis of multi-procedure programs. Abstract interpretation of programs:Correctness of abstract interpretation, Galois connections, dataflow analysis as an abstract interpretation.Type inference: Hindley-Milner's type inference algorithm for functional programs, subset-based andunification-based type inference for imperative programs. Pointer analysis.

References:

Flemming Nielson, Hanne Riis Nielson, and Chris Hankin: Principles of Program Analysis, Springer,(Corrected 2nd printing, 452 pages, ISBN 3-540-65410-0), 2005.Benjamic Pierce: Types and Programming Languages, Prentice-Hall India, 2002.Research papers

E0 229 (3:1) Algorithms in Coding priti


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Introduction to Algebra: Groups, Rings, Fields and Vector Spaces. Introduction to linear block codes,Hamming, BCH and Reed Solomon Codes. Bounds, MacWilliams Identities. Algebraic decoding algorithms:Peterson, Berlekamp-Massey, Berlekamp-Welch, Euclid decoding algorithms and the relationship betweenthem. Low density parity check codes. Tanner Graphs. Decoding on graphs using message passingalgorithms. Expander codes and their construction. List decoding algorithms.

References:

W.W. Peterson and E.J. Weldon, Error Correcting Codes, M.I.T. Press, 1972.Richard Blahut, Algebraic Codes for Data Transmission, Cambridge University Press, 2003.Rudiger Urbanke, Modern Coding Theory, (preprint), 2006.Current literature

E0 230 (3:1) Computational Methods of Optimization

Shirish K. Shevade /

Chiranjib Bhattacharyya / V.

Susheela Devi

Need for unconstrained methods in solving constrained problems. Necessary conditions of unconstrainedoptimization, Structure of methods, quadratic models. Methods of line search, Armijo-Goldstein and Wolfeconditions for partial line search. Global convergence theorem, Steepest descent method. Quasi-Newtonmethods: DFP, BFGS, Broyden family. Conjugate-direction methods: Fletcher-Reeves, Polak-Ribierre.Derivative-free methods: finite differencing. Restricted step methods. Methods for sums of squares andnonlinear equations. Linear and Quadratic Programming. Duality in optimization.

References:

Fletcher R., Practical Methods of Optimization, John Wiley, 2000.

E0 231 (3:1) Algorithmic Algebra Ambedkar Dukkipati

Basic algebraic notions: Integers, Euclidean algorithm, division algorithm, ring and polynomial rings,abstract orders and Dickson’s lemma; Introduction to Gröbner bases: Term orders, multivariate divisionalgorithm, Hilbert basis theorem, Gröbner bases and Buchberger algorithm, computation of syzygies, basicalgorithms in ideal theory, universal Gröbner bases; Algebraic Applications: Hilbert nullstellensatz,implicitization, decomposition, radical and zeros of ideals; Other applications: Toric ideals and integerprogramming, applications to graph theory, coding, cryptography, statistics.

References:

Ideals, Varieties and Algorithms by D. Cox and O’Shea, Springer; 2nd ed. 1997.Algorithmic Algebra by Bhubaneswar Mishra, Springer, 1993.

E0 232 (3:1) Probability and StatisticsIndrajit Bhattacharya /

Ambedkar Dukkipati

Probability spaces and continuity of probability measures, random variables and expectation, momentinequalities, multivariate random variables, sequence of random variables and different modes ofconvergence, law of large numbers, Markov chains, statistical hypothesis testing, exponential models,introduction to large deviations.

References:

An Introduction to Probability and Statistics by Vijay K. Rohatgi, A. K. Md. Ehsanes Saleh, Wiley,2nd edition 2000.An Intermediate course in Probability, by Allen Gut, Springer, 2008.

E0 233 (3:1)Information Theory, Inference and Learning

AlgorithmsAmbedkar Dukkipati

Data compression and Kraft's inequality, source coding theorem and Shannon entropy, Kullback-Leiblerdivergence and maximum entropy, I-projections and Sanov theorem, Kullback- siszar iteration and iterativescaling algorithms, Fisher information and Cramer-Rao inequality, quantization and introduction to ratedistortion theory, generalized information measures and power-law distributions.

References:

Elements of Information Theory, by T. M. Cover and J. A. Thomas, John Wiley & Sons, 2nd edition,2006.Information Theory, Inference, and Learning Algorithms by D.J.C. MacKay, Cambridge UniversityPress, 2003.

E0 235 (3:1) Cryptography Sanjit Chatterjee


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Elementary number theory, Finite fields, Arithmetic and algebraic algorithms, Secret key and public keycryptography, Pseudo random bit generators, Block and stream ciphers, Hash functions and messagedigests, Public key encryption, Probabilistic encryption, Authentication, Digital signatures, Zero knowledgeinteractive protocols, Elliptic curve cryptosystems, Formal verification, Cryptanalysis, Hard problems.

References:

Stinson. D. Cryptography: Theory and Practice.Menezes. A. et. al. Handbook of Applied Cryptography

E0 236 (3:1) Information Retrieval M. Narasimha Murty

Information retrieval using the Boolean model. The dictionary and postings lists. Tolerant retrieval. Indexconstruction and compression. Vector space model and term weighting. Evaluation in information retrieval.Relevance feedback and query expansion. Probabilistic information retrieval. Language models forinformation retrieval. Text classification and clustering. Latent semantic indexing. Web search basics. Webcrawling and indexes. Link analysis.

References:

C. D. Manning, P. Raghavan, and H. Schutze, Introduction to Information Retrieval, CambridgeUniversity Press, 2008.Recent Literature

E0 237 (2:1) Intelligent Agents V. Susheela Devi

Concepts of Agency and Intelligent Agents. Action of Agents, Percepts to Actions. Structure of IntelligentAgents, Agent Environments, Communicating, Perceiving, and Acting. Concepts of Distributed AI,Cooperation, and Negotiation. Applications: Web-based Agents, Database Applications. Agent Programming

References:

S. Russel and P. Norvig, Artificial Intelligence - A Modern Approach, Prentice Hall, 1995.Recent Papers

E0 238 (3:1) Artificial IntelligenceM. Narasimha Murty / V.

Susheela Devi

Introduction to Artificial Intelligence, Problem solving, knowledge and reasoning, Logic, Inference,Knowledge based systems, reasoning with uncertain information, Planning and making decisions, Learning,Distributed AI, Communication, Web based agents, Negotiating agents, Artificial Intelligence Applicationsand Programming.

References:

S. Russel and P. Norvig, Artificial Intelligence - A Modern Approach, Prentice Hall, 1995.George F. Luger, Artificial Intelligence, Pearson Education, 2001.Nils J. Nilsson, Artificial Intelligence - A New Synthesis, Morgan Kaufmann Publishers, 2000

E0 240 (3:1) Modeling and Simulation

Chiranjib Bhattacharyya /

Matthew Jacob

Thazhuthaveetil

Introduction to Probability theory, Random variables, commonly used continuous and discrete distributions.Introduction to Stochastic Process, Poisson process, Markov chains, steady stateand transient analysis.Psuedo random numbers: Methods of Generation and testing. Methods for generating continuous anddiscrete distributions. Methods for generating Poisson Process. Building blocks of Simulation, DataStructures and Algorithms. Introduction to Probabilistic modelling, Maximum Likelihood Variance reductiontechniques: antithetic variates, control variates, common random numbers, importance sampling. Analysisof Simulation results: confidence intervals, design of experiments. Markov Chain Monte Carlo techniques.

References:

Sheldon M. Ross: Introduction to Probability Models 7th Edition, Academic Press, 2002Donald E. Knuth: The Art of Computer Programming - Volume 2: Semi Numerical Algorithms, 2ndEdition, Addison Wesley, Reading MA, USA 2000Sheldon M. Ross Simulation 3rd Edition, Academic Press, 2002A. M. Law and W. D. Kelton: Simulation Modeling and Analysis, 3rd Edition, McGrawHill, New York,USA, 1998Raj Jain The Art of Computer Systems Performance Analysis, John Wiley and Sons, New York, USA,1991


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E0 241 (3:1) Computer Communication Networks Shalabh Bhatnagar

Introduction to computer networks; telephone networks, networking principles; switching - circuitswitching, packet switching; scheduling - performance bounds, best effort disciplines, naming andaddressing, protocol stack, SONET/SDH; ATM networks - AAL, virtual circuits, SSCOP; Internet -addressing, routing, end point control; Internet protocols - IP, TCP, UDP, ICMP, HTTP; performanceanalysis of networks - discrete and continuous time Markov chains, birth-death processes, timereversibility, queueing / delay models - M/M/1, M/M/m, M/M/m/m, M/G/1 queues, infinite server systems;open and closed queueing networks, Jackson's theorem, Little's law; traffic management - models, classes,scheduling; routing algorithms - Bellman Ford and Dijkstra's algorithms; multiple access, frequency andtime division multiplexing; local area networks - Ethernet, token ring, FDDI, CSMA/CD, Aloha; control ofnetworks - QoS, window and rate congestion control, open and closed loop flow control, large deviations ofa queue and network, control of ATM networks.

References:

I. Mitrani, Modelling of Computer and Communication Systems, Cambridge, 1987.J.Walrand and P.Varaiya, High Performance Communication Networks, Harcourt Asia (MorganKaufmann), 2000.S.Keshav, An Engineering Approach to Computer Networking, Pearson Education, 1997.D.Bertsekas and R.Gallager, Data Networks, Prentice Hall of India, 1999.J.F.Kurose and K.W.Ross, Computer Networking: A Top-Down Approach Featuring the Internet,Pearson Education, 2001.

E0 242 (3:1) Probabilistic Models for Learning Chiranjib Bhattacharyya

Review of Probability theory: Random variables, Expectation, Central Limit theorem. Latent variablemodels: mixture models, HIdden Markov models, EM algorithm, Graphical models: Algorithms forInference, Markov Chain Monte Carlo Methods, Belief Propagation, Variational methods Factor Analysis.Applications to Text: Maxent Formalism, Statistical Parsing, CKY algorithm, Topic models.

References:

Sheldon Ross - Introduction to Probability theoryC. Bishop - Pattern RecognitionJ. Pearl: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible InferenceC. Manning and H. Schutzle - Foundations of Statistical Natural Language Processing

E0 243 (3:1) Computer ArchitectureR. Govindarajan / Matthew

Jacob Thazhuthaveetil

Processor architecture, pipelining, vector processing, superscalar processors, hardware and compilersupport for branch prediction, out-of-order Instruction issue, speculative execution and other techniquesfor high-performance, Instruction and data cache organizations, multilevel caches, parallel memorysystems, Support for virtual memory, Multiple processor systems, taxonomy, programming models,message passing systems, Interconnection networks, shared memory system, memory models, cachecoherence, I/O systems, parallel disk organisations, Introduction to advanced topics.

References:

Hennessy, J.L., and Patterson, D.A.: Computer Architecture, A quantitative Approach, MorganKaufmann.Stone, H.S.: High-Performance Computer Architecture, Addison-Wesley.Current literature

E0 245 (3:0) Fault Tolerant Computing Lawrence Jenkins

Redundancy techniques, fault coverage, computational integrity, fault detection methods fault identificationalgorithms, exception handling, damage assessment and confinement, system diagnosability, diagnosisalgorithms, system recovery and distribution, reconfiguration techniques, repairable systems, algorithmsbased fault tolerance testing techniques, test scheduling, test pattern generation, fault tolerant computercommunication networks, fault tolerance of software.

References:

Anderson, L., and Lee, P. A., Fault Tolerance, Principles and Practice, Prentice Hall, 1981.Siework, C. P. and Swartz, R. S., Theory and Practice of Reliable System Design, Mc-Graw Hill,1982.Current Literature.

Prerequisites

E0 241.


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E0 246 (3:0) Real-time Systems Lawrence Jenkins

Hard and soft real-time systems, deadlines and timing constraints, workload parameters, periodic taskmodel, precedence constraints and data dependency, real time scheduling techniques, static and dynamicsystems, optimality of EDF and LST algorithms, off-line and on-line scheduling, clock driven scheduling,cyclic executives, scheduling of aperiodic and static jobs, priority driven scheduling, fixed and dynamicpriority algorithms, schedulable utilization, RM and DM algorithms, priority scheduling of aperiodic andsporadic jobs, deferrable and sporadic servers, resource access control, priority inversion, priorityinheritance and priority ceiling protocols, real-time communication, operating systems.

References:

Jane W. S. Liu, Real-Time Systems, Pearson Education, New Delhi, 2001.Current literature

E0 247 (3:0) Sensor Networks Lawrence Jenkins

Basic concepts and issues, survey of applications of sensor networks, homogeneous and heterogeneoussensor networks, topology control and clustering protocols, routing and transport protocols, access controltechniques, location awareness and estimation, security information assurance protocols, data fusion andmanagement techniques, query processing, energy efficiency issues, lifetime optimization, resourcemanagement schemes, task allocation methods, clock synchronization algorithms. Tiny operating system,middleware support, simulation packages.

References:

C. S. Raghavendra, K. M. Shivalingam and T. Znati, Wireless Sensor Networks, Springer, New York,2004.F. Zhao and L.Guibas, Wireless Sensor Networks, An Information processing Approach, MorganKauffmann, San Fransisco 2004.Current literature

Prerequisites

Consent of Instructor

E0 251 (3:1) Data Structures and AlgorithmsV. Susheela Devi / M.

Narasimha Murty

Abstract data types and data structures, Classes and objects, Complexity of algorithms: worst case,average case, and amoritized complexity. Algorithm analysis. Algorithm Design Paradigms. Lists: stacks,queues, implementation, garbage collection. Dictionaries: Hash tables, Binary search trees, AVL trees,Red-Black trees, Splay trees, Skip-lists, B-Trees. Priority queues. Graphs: Shortest path algorithms,minimal spanning tree algorithms, depth-first and breadth-first search. Sorting: Advanced sorting methodsand their analysis, lower bound on complexity, order statistics.

References:

A.V. Aho, J.E. Hopcroft, and J.D. Ullman, Data Structures and Algorithms, Addison Wesley, ReadingMassachusetts, USA, 1983T.H. Cormen, C.E. Leiserson, and R.L. Rivest, Introduction to Algorithms, The MIT Press, Cambridge,Massachusetts, USA, 1990M.A. Weiss, Data Structures and Algorithms Analysis in C++, Benjamin/Cummins, Redwood City,California, USA, 1994.

E0 253 (3:1) Operating Systems K. Gopinath / R.C. Hansdah

User Level Specification of OS. Fundamental Concepts of Multiprogrammed OS, Basic Concepts andTechniques for Implementation of Multiprogrammed OS. Processes and the Kernel, Microkernel Architectureof OS. Multiprocessor, Multimedia, and Real-Time OS. POSIX Standards. Management and Control ofProcesses. Basic Concept of Threads, Types of Threads, Models of Thread Implementations. Traditional andReal-Time Signals. Clocks, Timers and Callouts. Thread Scheduling for Unix, Windows, and Real-Time OS,Real-Time Scheduling. Interprocess/Interthread Synchronization and Communication, MutualExclusion/Critical Section Problem, Semaphores, Monitors, Mailbox, Deadlocks. Concepts andImplementation of Virtual Memory(32-bit and 64-bit), Physical Memory Management. File Organization, FileSystem Interface and Virtual File Systems, Implementation of File Systems. I/O Software:Interrupt ServiceRoutines and Device Drivers. Protection and Security. Case Study of Unix, Windows, and Real-Time OS.

References:

Andrew S. Tanenbaum: Modern Operating Systems, Second Edition, Pearson Education, Inc., 2001.Uresh Vahalia: UNIX Internals: The New Frontiers, Prentice-Hall, 1996.J. Mauro and R. McDougall: Solaris Internals: Core Kernel Architecture, Sun Microsystems Press,


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2001.Daniel P. Bovet and Marco Cesati: Understanding the Linux kernel, 2nd Edition O'Reilly & Associates,Inc., 2003.

E0 254 (3:1) Network and Distributed Systems Security R.C. Hansdah

Security Goals and Violations; Security Requirements; Security Services; Discrete Logs,Encryption/Decryption Functions, Hash Functions, MAC Functions; Requirements and AlgorithmicImplementation of One-Way Functions; OS Security Violations and Techniques to Prevent Them; AccessControl Models; Secure Programming Techniques; Authenticated Diffie-Hellman Key EstablishmentProtocols; Group Key Establishment Protocols; Block Ciphers and Stream Ciphers; Modes of Encryption;Digital Signatures; Authentication Protocols; Nonce and Timestamps; PKI and X.509 AuthenticationService; BAN logic; Kerberos; E-mail Security; IP Security; Secure Socket Layer and Transport LayerSecurity; Secure Electronic Transactions; Intrusion Detection; Malicious Software Detection; Firewalls.

References:

William Stallings: Cryptography and Network Security: Principles and Practices, Fourth Edition,Prentice Hall, 2006.Neil Daswani, Christoph Kern and Anita Kesavan: Foundations of Security: What Every ProgrammerNeeds to Know, Published by Apress, 2007.Yang Xiao and Yi Pan: Security in Distributed and Networking Systems, World Scientific, 2007.Current Literature.

Prerequisites

Knowledge of Java is desirable, but not necessary.

E0 255 (3:1) Compiler DesignY.N. Srikant / Uday Kumar

Reddy B. / priti

Review of syntax analysis and use of tools LEX and YACC; symbol tables and semantic analysis; run timestorage administration and intermediate code generation; dataflow analysis, code optimization and registerallocation; instruction selection and code generation; machine dependent optimizations for pipelined, andclustered architectures.

References:

Aho, A.V., Ravi Sethi and J.D. Ullman: Compilers - Principles, Techniques and Tools, AddisonWesley, 1988.S. Muchnick: Advanced Compiler Design and Implementation, Morgan Kauffman, 1998Selected Papers.

E0 256 (3:1)Distributed Embedded Systems Development:

Rigorous Modeling and design methodologiesS. Ramesh / Y.N. Srikant

Basics of Embedded Systems: Introduction, Examples, Salient features, Importance, Small and LargeApplications, Cyber-physical systems; Overview of design of embedded systems: Challenges and need forrigorous methods; Introduction to Model Based development of Embedded Systems: Functional ModelDevelopment and Analysis, Distributed Platform Model Development and Analysis, Allocation andScheduling of function on Distributed Platform, Performance Analysis and Design Space Exploration;Functional Models of Embedded Systems: Finite State Machines and Statecharts, Data Flow Process Graphsand, Communicating Finite State Machines, Synchronous Reactive Models, Illustrative introduction tovarious modeling languages like Simulink/SF, Esterel and SCADE; Model Simulation and Verification: Modelexecution, HW and Plant in loop simulation, Property Specification and design verification, Model basedtesting; Implementation Platform Models: Centralized and Distributed Platforms, Event and Time TriggeredSystems, RTOS and Middleware, Various bus protocols: CAN, TT and Flexray Periodic and sporadic Tasks,Task Communication, dependency graphs; Task Scheduling: Fixed priority Scheduling, Priority inversionand ceiling, Rate monotonic, Deadline Monotonic and Dynamic (EDF) scheduling, Utility based andResponse Time Analysis; Distributed Scheduling: Task and message scheduling, Holistic scheduling,Scheduling of dependent tasks; Design methodologies and tools: Overview of some well-known tools whichinclude Metropolis (UC Berkeley), Time-weaver (CMU) and TTTech Tools (TU Vienna); Advanced Topics:Emerging Component based methodologies, Fault-Tolerant implementations.

References:

H. Kopetz, Real-Time Systems: Design Principles for Distributed Embedded Applications, KluwerAcademic Publishers, 1997.Frank Vahid and Tony Givargis, Embedded System Design, John Wiley, 2002.Balarin et al., Hardware-Software Codesign of Embedded System Design: A POLIS approach,Kluwer, 1997.D. Gajski, et al., Specification and Design of Embedded Systems, Prentice-Hall, 1994.Stephen A. Edwards. Languages for Digital Embedded Systems. Kluwer, 2000.


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S. Edwards, L. Lavagno, E. A. Lee, and A. Sangiovanni-Vincentelli, Design of Embedded Systems:Formal Methods, Validation and Synthesis, Proceedings of the IEEE, vol. 85, no. 3, pp. 366-390,1997.W. Damm, et al. Mapping task-graphs on distributed ECU networks: Efficient algorithms forfeasibility and optimality, in IEEE International Conference on Embedded and Real-Time ComputingSystems and Applications (RTCSA), 2006.P. Pop, P. Eles, and Z. Peng, Schedulability-driven communication synthesis for time-triggeredembedded systems, Real-Time Systems, vol. 26, no. 3, pp. 297-325, 2004.K. Tindell and J. Clark, Holistic schedulability analysis for distributed hard real-time systems,Microprocessing and Microprogramming, vol. 40, no. 2-3, pp.117 . 134, 1994.K. Tindell, H. Hanssmon, and A. J. Wellings, Analysing real-time communications: Controller AreaNetwork (CAN),in IEEE Real-Time Systems Symposium (RTSS), San Juan, Puerto Rico, 1994.R. K. Shyamasundar and S. Ramesh, Real-Time Programming: Semantics and Verification, to bepublished in World Scientific Press.

Prerequisites

Basic Operating System and Computer Architecture courses at UG LevelDesirable: Software Engineering at UG/PG level

E0 257 (3:1) Software Architecture Raghu Hudli / Y. N. Srikant

Software process and the role of modeling and analysis, software architecture, and software design.Software Modeling and Analysis: analysis modeling and best practices, traditional best practice diagramssuch as DFDs and ERDs, UML diagrams and UML analysis modeling, analysis case studies, analysis tools,analysis patterns. Software Architecture: architectural styles, architectural patterns, analysis ofarchitectures, formal descriptions of software architectures, architectural description languages and tools,scalability and interoperability issues, web application architectures, case studies. Software Design: designbest practices, design patterns, extreme programming, design case studies, component technology, objectoriented frameworks, distributed objects, object request brokers, case studies.

References:

Booch,G., Rumbaugh, J., Jacobson, I., The Unified Modeling Language User Guide, Addison- Wesley,1999.Gamma, E.,Helm, R. Johnson, R. Vissides, J., Design Patterns, Elements of Reusable Object-Oriented Software, Addison-Wesley, 1995.Frank Buschmann et al. Pattern Oriented Software Architecture, Volume 1: A System of Patterns.John Wiley and Sons, 1996.Shaw, M., and Garlan, D., Software Architecture: Perspectives on an Emerging Discipline,Prentice-Hall, 1996.Len Bass et al. Software Architecture in Practice. Addison Wesley, 1998.

E0 258 (3:1) Foundations of Programming Languages Raghu Hudli / Y. Narahari

Survey of programming paradigms and computational models for program execution. Programminglanguage examples, syntax description and language semantics Functional programming, lamda calculus,Higher-order functions, currying, recursion. Imperative programming and control structures, invariants,object models, messages, and method dispatch, inheritance, subtypes and subclasses, polymorphism,covariance, and contravariance. Formal aspects of Java. Concurrent programming models and constructs,programming in the multi-core environment. Introduction to Logic programming quantifiers, first orderlogic, Horn clauses, unification and resolution.

References:

Daniel Friedman, Mitchel Wand and Christopher Hanes. "Essentials of Programming Langauges",Prentice Hall of India, 2nd Edition, 2001.John Reynolds, "Theories of Programming Languages", Cambridge Univ. Press, 1998.John Mitchell, "Concepts in Programming Languages", Cambridge Univ. Press, 2003.Benjamin Pierce, "Types and and Programming Languages", MIT Press, 2002.Selected Chapters from J. an Leeuwen, Ed. "Handbook of Theoretical Computer Science", Vol. B,Elsevier, MIT Press, 1994.Kim Bruce, "Foundations of Object Oriented Languages", Prentice Hall of India, 2002.Martin Abadi and Luca Cadelli, "A Theory of Objects", Springer, 1996.Current research papers and Internet resources

E0 261 (3:1) Database Management Systems Jayant R. Haritsa

Design of Database Kernels, Query Optimization (Rewriting Techniques, Access Methods, Join Algorithms,Plan Evaluation), Transaction Management (ARIES), Distributed Databases (Query Processing andOptimization, Concurrency Control, Commit Protocols), Object-Relational Databases (Motivation, Designand Implementation), Spatial Databases (Storage, Indexing Techniques, Query Optimization), Data Mining


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(Association, Classification and Sequence Rules, Integration with Database Engines), Data Warehousing(Star and Snowflake Schemas, Data Cubes, View Maintenance), Semistructured and Web Databases (DataModels, Query Systems, XML, XML-Schema, Relational Storage, Compression), Mobile Databases(Broadcast Disks, Indexing Techniques), Applications to E-commerce.

References:

Fundamentals of Database Systems R. Elmasri and S. B. Navathe, Addison-Wesley, 3rd ed., 1999.Database Management Systems R. Ramakrishnan and J. Gehrke, McGraw-Hill, 2nd ed., 1999.Readings in Database Systems M. Stonebraker and J. Hellerstein, Morgan Kaufmann, 3rd ed., 1998.Object-Relational DBMSs M. Stonebraker, Morgan Kaufmann, 1996 .Data Warehousing (Strategies, Technologies and Techniques) R. Mattison, IEEE Press, 1998.Data Mining R. Groth, Prentice Hall, 1998.Recent Conference and Journal papers.

Prerequisites

Data Structures, C or C++, Undergraduate course in DBMS

E0 262 (3:0) Multimedia Information SystemsP Venkataram / Anandi

Giridharan

Multimedia information, delay-sensitive and time-based media data modeling, multimedia storage andretrieval techniques, multimedia communications: synchronization, delay compensation, QoS managementand negotiation protocols, architectures and issues for distributed multimedia systems, prototypemultimedia systems: video-on-demand, video conferencing, wireless multimedia.

References:

P. Venkataram, Design Aspects of Multimedia Information Systems, Pearson Publishers, 2009.W. I. Grosky, R. Jain and R. Mehrotra, The Hand Book of Multimedia Information Management,Prentice-Hall, 1997.J. F. Koegel Buford, Multimedia Systems, Addison-Wesley, 1994.Relevant Research Papers from the Journals/Conferences.

E0 264 (3:1) Distributed Computing Systems R.C. Hansdah

Fundamental Issues in Distributed Systems, Distributed System Models and Architectures; Classification ofFailures in Distributed Systems, Basic Techniques for Handling Faults in Distributed Systems; Logical Clocksand Virtual Time; Physical Clocks and Clock Synchronization Algorithms; Security Issues in ClockSynchronization; Secure RPC and Group Communication; Group Membership Protocols and Security Issuesin Group Membership Problems; Naming Service and Security Issues in Naming Service; Distributed MutualExclusion and Coordination Algorithms; Leader Election; Global State, Termination and DistributedDeadlock Detection Algorithms; Distributed Scheduling and Load Balancing; Distributed File Systems andDistributed Shared Memory; Secure Distributed File Systems; Distributed Commit and Recovery Protocols;Security Issues in Commit Protocols; Checkpointing and Recovery Protocols; Secure Checkpointing; Fault-Tolerant Systems, Tolerating Crash and Omission Failures; Implications of Security Issues in DistributedConsensus and Agreement Protocols; Replicated Data Management; Self-Stabilizing Systems; DesignIssues in Specialized Distributed Systems.

References:

Randy Chow, and Theodore Johnson, "Distributed Operating Systems and Algorithms", Addison-Wesley, 1997.Sukumar Ghosh, "Distributed Systems: An Algorithmic Approach", CRC Press, 2006.Kenneth P. Birman, "Reliable Distributed Systems: Technologies, Web Services, and Applications",Springer New York, 2005.G. Coulouris, J. Dollimore, and T. Kindberg, "Distributed Systems: Concepts and Designs", FourthEdition, Pearson Education Ltd., 2005.Current Literature

Prerequisites

NDSS(E0 254) or equivalent course

E0 265 (3:0) Multimedia Systems K. R. Ramakrishnan

Introduction: video, Audio. Image compression: JPEG, GIF. Video compression: MPEG-1, -2, -4, and -7,H.261. MPEG audio compression, AC 3, content based retrieval, multimedia networking: ATM, RTP, RSVP,RTSP; multicasting: storage and server issues, multimedia processors, mobile multimedia, watermarking,multimedia systems: VoD, video and conferencing, HDTV.

References:


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Raghavan, S. V. and Tripathi, S. K., Networked Multimedia Systems: Concepts, Architecture andDesign.Raif Steinmetz, Klara Nahrtedt, Multimedia: Computing, Communication and Application, PrenticeHall, 1995.

Prerequisites

Basic knowledge of DSP and Programming.

E0 266 (3:0) Topics in Ubiquitous Computing P. Venkataram

Definition and scope of ubiquitous computing, essential elements of ubiquitous networks, architecture forubiquitous computing: new devices and communications; and software architectures. Integrating thephysical and the virtual worlds: sensing and actuation; ontology and modeling the world; awareness andperception. Interactions between humans and (ubiquitous) computers: situated (context-aware)computing; multimodal and natural interaction; disambiguation and proactivity. Social aspects of ubiquitouscomputing: implications on privacy, security and autonomy; system and legal safeguards; cost-benefit andmarket focus. Ubiquitous applications: the appropriate design; Weiser’s vision of ubiquitous computing;context awareness; mixed reality and sensible design. Illustration of some existing application domains forubiquitous computing in such areas as gaming, workplaces, domestic spaces, museums and educationalcommunities.

References:

Research papers on Ubiquitous Computing.

Prerequisites

Communication Protocols/Computer Networks.

E0 268 (3:1) Data MiningShirish K. Shevade / M.

Narasimha Murty

Introduction to data mining. Data preprocessing and cleaning. Data visualization and exploratory dataanalysis. Data mining techniques. Performance evaluation. Finding patterns and rules. Predictive anddescriptive modeling. Issues relating to large data sets. Applications to Web Mining and Bioinformatics.

References:

Tan P.-N, Steinbach M. and Kumar V.: Introduction to Data Mining, Addison-Wesley, 2006.Current Literature.

E0 269 (3:1) Probabilistic Graphical Models Indrajit Bhattacharya

Graph types : conditional independence; directed, undirected, and actor models; algorithms for conditionalindependence (e.g., Bayes-ball,d-separation, Markov properties on graphs, factorization,Hammersley-Clifford theorems). Static Models : linear Gaussian models, mixture models, factor analysis, probabilisticdecision trees, Markov Random Fields, Gibbs distributions, static conditional random fields (CRFs),multivariate Gaussians as graphical models, Exponential family, generalized linear models, factoredexponential families. Dynamic (temporal) models : Hidden Markov Models, Kalman filtering and linear-Gaussian HMMs, linear dynamic models, dynamic Bayesian networks (DBNs), label and observation bias innatural language processing, dynamic conditional random fields (CRFs), and general dynamic graphicalmodels. Chordal Graph Theory : moralization; triangulated, decomposable, and intersection graphs,Tree-width and path-width parameters of a graph. Exact Probabilistic Inference : The elimination family ofalgorithms. Relation to dynamic programming. Generality (such as Viterbi, MPE, the fast Fouriertransform). junction trees, belief propagation, optimal triangulations. NP hardness results. ApproximateProbabilistic Inference : loopy belief propagation (BP), expectation propagation (EP), Sampling (markovchains, metropolis hastings, gibbs, convergence and implementaional issues) particle filtering. StructureLearning : Chow Liu algorithm. Latent Dirichlet Allocation (1 wk): Exchangeability, de Finetti Theorem,Inference using collapsed Gibbs sampling, Dirichlet compound multinomial model.

References:

Probabilistic Graphical Models: Principles and Techniques", Daphne Koller and Nir FriedmanRelevant papers

Prerequisites

Introduction to Probability and Statistics and Consent of Instructor

E0 270 (3:1) Machine LearningShivani Agarwal / Indrajit

Bhattacharya


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Introduction to machine learning. Classification: nearest neighbour, decision trees, perceptron, supportvector machines, VC-dimension. Regression: linear least squares regression, support vector regression.Additional learning problems: multiclass classification, ordinal regression, ranking. Ensemble methods:boosting. Probabilistic models: classification, regression, mixture models (unconditional and conditional),parameter estimation, EM algorithm. Beyond IID, directed graphical models: hidden Markov models,Bayesian networks. Beyond IID, undirected graphical models: Markov random fields, conditional randomfields. Learning and inference in Bayesian networks and MRFs: parameter estimation, exact inference(variable elimination, belief propagation), approximate inference (loopy belief propagation, sampling).Additional topics: semi-supervised learning, active learning, structured prediction.

References:

Bishop. C M, Pattern Recognition and Machine Learning. Springer, 2006.Duda, R O, Hart P E and Stork D G. Pattern Classification. Wiley-Interscience, 2nd Edition, 2000.Hastie T, Tibshirani R and Friedman J, The Elements of Statistical Learning: Data Mining, Inferenceand Prediction. Springer, 2nd Edition, 2009.Mitchell T, Machine Learning. McGraw Hill, 1997.Current literature.

Prerequisites

Probability and Statistics (or equivalent course elsewhere). Some background in linear algebra andoptimization will be helpful.

E0 271 (3:1) Computer Graphics Vijay Natarajan

Principles of computer graphics; graphics pipeline; graphics hardware; transformations; viewing; lighting;shading; modeling; selected topics in meshing, subdivision techniques, multi-resolution methods,visualization, ray tracing; individual projects.

References:

Edward S. Angel. Interactive Computer Graphics, A top-down approach with OpenGL. Addison-Wesley, 2005.OpenGL Architecture Review Board, Dave Shreiner, Mason Woo, Jackie Neider, and Tom Davis.OpenGL Programming Guide: The Official Guide to Learning OpenGL. Addison-Wesley, 2005.Donald Hearn and M. Pauline Baker. Computer Graphics with OpenGL. Prentice Hall, 2003.

Prerequisites

Courses in linear algebra, data structures, algorithms, and programming.

E0 272 (3:1) Formal Methods in Software Engineering

K.V. Raghavan / Deepak D

Souza / Prahladavaradhan

Sampath

Modeling and analyzing state-based software systems using first-order predicate logic and relationalcalculus -- the tool Alloy. Detailed design of state-based systems, and code generation -- Eclipse ModelingFramework (EMF). Modeling and analysis of concurrent systems -- Promela. Code development usingrefactoring -- Eclipse Refactorings. Identifying errors in code during development using type inference,logical reasoning, and dataflow analysis -- JQual, ESC/Java, and FindBugs. Model checking, and exhaustiveexploration of applications -- CBMC, Pex. Software development by refinement of mathematicalabstractions -- Rodin.

References:

Logic in Computer Science: Modelling and Reasoning about Systems, by Michael Huth and MarkRyan.Software Abstractions: Logic, Language, and Analysis, by Daniel Jackson.Research papers.

E0 284 (3:0) Digital VLSI Circuits Bharadwaj Amrutur

Introduction to MOS transistor theory, circuit characterization & simulation, theory of logical effort,interconnect design and analysis combinational circuit design, sequential circuit design. Designmethodology & tools, testing & verification, datapath subsystems, array subsystems, power and clockdistribution, introduction to packaging.

References:

N.Weste and D. Harris, CMOS VLSI Design. A Circuits and Systems Perspective, Addison Weley,2005.J. M. Rabaey, A. Chandrakasan, and B. Nikolic, Digital Integrated Circuits.


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E0 285 (3:0) Computer Aided Design of VLSI systemsVirendra Singh / S. K.

Nandy

Introduction to VLSI CAD: Motivating factors and some trends; Digital hardware modeling: Logic andsystem level modeling, hardware description languages (VHDL and Verilog), RTL simulation; synchronousand asynchronous system design; verification methodology: Simulatin, BDD, Formal methods; logicsynthesis: technology mapping, ASIC design methodology, FPGA based designs and prototyping; Layoutsynthesis: The physical design, Timing analysis; Graph Algorithms and their applications in IC design; CADtools and their use; Design for testability; system level design: may have a brief mention of system C andsystem Verilog.

E0 290 (3:1)Mathematical Foundations for Modern

Computingkannan

High-Dimensional Data. Modeling of data in a high dimensional space. High dimensional Euclideangeometry. Random projection theorem. Random Graphs. Erdos-Renyi model, Properties of random graphs,Giant component and threshold phenomena. Random satisfiability problems. Singular Value Decompositionand its applications. Random Walks: Connections to electrical networks, convergence using eigen valuesand conductance measures. Foundations of Learning Theory. The perceptron algorithm, margins andsupport vector machines and Vapnik-Chervonenkis theorem and applications. Clustering algorithms andcriteria. Provable results and algorithms for k-means and other criteria. Recent work on finding localcommunities using random walks. Massive Data Computations including streaming algorithms. Fastapproximations to matrices such as the CUR approximation. Games and Economic related models andalgorithms, the notion of equilibrium, its existence and computation, markets and market equilibria.

References:

John Hopcroft and Ravi Kannan. Mathematics for Modern Computing. Forthcoming chapters will bemade available.Ravi Kannan and Santosh Vempala. Spectral Algorithms, Now Publishers, 2009.

E0 291 (3:1) Spatial DatabasesJayant R. Haritsa / N. L.

Sarda

1. Introduction, Motivation: Application Domains of Geographical Information Systems (GIS), Common GISdata types and analysis, OGC standards and reference geometry model 2. Models of Spatial Data :Conceptual Data Models for spatial databases (e.g. pictogram enhanced ERDs). Logical data models forspatial databases: raster model (map algebra), vector model (OGIS/SQL1999) 3. Spatial query languages :Need for spatial operators and relations, SQL3 and ADT. Spatial operators, OGIS queries. 4. Spatial storageMethods : Clustering methods (space filling curves), Storage methods (R-tree, Grid files), Concurrencycontrol (R-link trees), Compression methods for raster and vector data, Spatial indexing 5. Spatio-temporaland moving object databases : Spatio Bitemporal objects and operations. Querying, Event models. Spatiotemporal indexes 6. Processing spatial queries : Spatial selection, joins, aggregates, nested queries, buffers7. Query processing and optimization : strategies for range query, nearest neighbor query, spatial joins(e.g. tree matching), cost models for new strategies, impact on rule based optimization, selectivityestimation 8. Spatial networks : Road network databases and connectivity graphs. Topology storage, queryfor spatial networks. 9. Mining spatial databases : Clustering, Spatial classification, Co-location patterns,Spatial outliers, 10. Geosensor databases

References:

Spatial Databases: A Tour, S. Shekhar and S. Chawla, Prentice Hall, 2003Moving Objects Databases, by Ralf Hartmut Guting, Markus SchneiderMorgan kaufman, 2005Spatial Databases with Applications to GIS, P. Rigaux, M. Scholl, A. Voisard, Morgan Kaufmann,2002Spatio-Temporal Database, M. Koubarakis, T. Selles at al (ed.), Springer 2003Selected papers (see the bibliography available at: http://www.spatial.cs.umn.edu/Courses/Fall07/8715/paperList.html)

E0 292 (3:1) Mobile Application DevelopmentNigamanth Sridhar / K.

Gopinath

This course will cover topics on developing applications on mobile smartphone platforms. Primary emphasiswill be on Android development, while students will also learn the basics of developing applications for iOS.The course will include a project that will be defined and executed by student groups.

References:

The Android and iOS developer documentation.Lecture notes handed out in class.Papers from recent conferences and journals.

Prerequisites


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Programming skills.

E0 320 (3:1) Topics in Graph Theory L. Sunil Chandran

Minors: Introduction - properties which causes dense minors in graphs: average degree, girth, Wagner'scharacterisation of graphs without K5 minors. Tree Decompositions: treewidth, pathwidth, upper and lowerbounds for treewidth, relation of treewidth and minors, influence on algorithmic graph problems.Hadwiger's conjecture - its relation with the four colour theorem, related work.

References:

Graph Theory (Chapters 8 and 12), Reinhard Diestel, Springer, 2000.Current Literature

E0 323 (3:1) Topics in VerificationAditya Kanade / Dr. Satish

Chandra

In this course, we aim to study algorithmic approaches for automating 1. Synthesis of programs, 2.Discovering specifications of programs, 3. Selection of domain-specific algorithms. Along with presentationsby course instructors, every participant will be assigned a few papers to be presented in the class. Theexchange of knowledge will be mainly through open discussions in the classes. An optional course projectwill be offered for interested participants. The evaluation will be based on quality of presentations,understanding of material, and participation in the class discussions.

References:

A number of classic as well as recent research papers have been identified carefully. The list can bemade available if required. There are no specific text book references for the course.

Prerequisites

Program Analysis and Verification (E0 227) or Automated Verification (E0 223); in other cases, youcan seek permission from the instructors.

E0 325 (3:1) Topics in Algorithms T. Kavitha

Network algorithms and algebraic algorithms: Algebraic algorithms include algorithms for primality testing,factoring polynomials and the LLL algorithm. On network algorithms the focus will be on algorithms forflows, cuts, and connectivity problems concentrating on algorithms for global min cut (Gabow's treepacking algorithm, Karger's near linear time Monte Carlo algorithm), Steiner min cut (the Cole-Hariharanalgorithm), Edmonds' arborescences.

References:

Von Zur Gathen and Gerhard: Modern Computer Algebra Relevant literature

E0 327 (3:1) Topics in Program Analysis K.V. Raghavan

Software engineering topics: Introduction to frameworks - Eclipse and Wicket; Design patterns - ExtensionObject, Proxy, Virtual Proxy, Bridge, Composite, Visitor, Observer, Execute Around, Adapter, PluggableAdapter, Objectify Association, Builder, Strategy, Command, Memento; Architectural patterns - layers,model-view-controller; RESTful web programming. Computer science topics pertitent to verification ofobject-oriented and framework-based applications: Type states; behavioral subtyping; modellinglanguages; property verification - logical reasoning on pre- and post-conditions, model checking, typeinference, and abstract interpretation.

References:

Design Patterns Explained, 2/e: A New Perspective on Object-Oriented Design. Alan Shalloway andJames Trott, Dorling Kindersley (India Pvt Ltd, 2006.Contributing to Eclipse: Principles, Patterns, and Plugins. Erich Gamma and Kent Beck, DorlingKindersley (India) Pvt Ltd, 2007.Spring in action, updated for Spring 2.0, 2/e. Ryan Breidenbach and Craig Walls, Dreamtech Press(Wiley India), 2007.Wicket in action. Martijn Dashorst and Eelco Hillenius. Dreamtech Press (Wiley India), 2008.Restful Web Services. Leonard Richardson, Sam Ruby, and David Heinemeier Hansson. Shroff /O'Reilly, 2007.Current research papers.

Prerequisites

Experience in object-oriented programming, preferably using Java; basics of discrete structures andmathematical logic OR Consent of the instructor


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E0 330 (3:1) Convex Optimization Shirish K. Shevade

Convex sets and functions, Convex Optimization Problems, Duality, Approximation and fitting, StatisticalEstimation, Geometric Problems, Unconstrained minimization, Interior-point methods.

References:

S. Boyd and L. Vandenberghe: Convex Optimization, Cambridge University Press, 2004.

E0 335 (3:1) Topics in Cryptology Sanjit Chatterjee

The notion or definition of security and different formalizations of a notion and the question of equivalence;the structure of security proof (also known as security reduction). public key encryption: the notions ofindistinguishability and semantic security including the question of equivalence of definitions, securityagainst chosen pliantext and chosen ciphertext attacks. Some concrete public key encryption andidentity-based encryption schemes and their security. Digital signatures and the notion of existentialunforgability under chosen message attacks. Key agreement protocols and secure channels. The randomoracle assumption. The quantitative measure of security including the questions of tightness in securityreduction and concrete security.

References:

A selection of research papers from journals and conference proceedings.

Prerequisites

Cryptography (E0 235).

E0 343 (3:1) Topics in Computer Architecture

Matthew Jacob

Thazhuthaveetil / R.

Govindarajan

Architecture and harware description languages (RTL, ISPS, vhdl). Processor architecture, Instruction levelparallelism, Latency tolerance, multithreading, interconnection networks, Standards (bus, SCI),architectures, routing, Cache coherency, protocol specification, correctness, performance. Memoryconsistency models, synchronization primitives, parallel programming paradigms, I/O systems, Interfacestandards, parallel I/O, performance evaluation, analytical methods, simulation algorithms and techniques,benchmarking.

Prerequisites

Computer Architecture, Operating Systems, Some Familiarity with Analytical Performance EvaluationTechniques

E0 352 (3:1)Topics in System Research: Learning for

Computer System

K. Gopinath / Chiranjib

Bhattacharyya

Regression, feature selection, ensemble methods (boosting, bagging, etc) and HMM models. Selected topicsin OS (related to the papers under discussion and including, as necessary, some review of requiredbackground)

References:

Current Literature (Conference proceedings of SOSP, SysML, etc)

Prerequisites

Background in atleast one computer systems area like OS, Databases, Compilers etc. andInstructors' consent.

E0 353 (3:1) Topics in Operating Systems K. Gopinath

Selected topics in operating systems of topical interest. Design, implementation, correctness andperformance related aspects. Past offerings included study of subsystems such as process, storage andnetwork subsystems.

References:

Recent Literature

Prerequisites

Consent of instructor and a course in Operating Systems, Computer Architecture with somefamiliarity of the internals of Linux/Unix


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E0 355 (3:1) Topics in Compiler Design Y.N. Srikant

Dynamic and Just-In-Time compilation. Compilation for embedded systems: performance, memory, andenergy considerations. Static analysis: points-to analysis, abstract interpretation. WCET estimation. Typesystems. Optimizations for O-O languages. Compilation for multi-core systems.This course will be based on seminars and mini projects.

References:

Y.N. Srikant and Priti Shankar (ed.), The Compiler Design Handbook: Optimizations and MachineCode Generation, 2nd ed., CRC Press, 2008.

Prerequisites

Good knowledge of dataflow analysis and compiler optimizations

E0 358 (3:1)Advanced Techniques in Compilation and

Programming for Parallel ArchitecturesUday Kumar Reddy B.

Parallel architectures: a brief history, design, Auto-parallelization for multicores, GPUs, and distributedMemory clusters Lock-free and wait-free data structures/algorithms for parallel programming Study ofexisting languages and models for parallel and high performance programming; issues in design of newones.

References:

Aho, Lam, Sethi, and Ullman, Compilers: Principles, Techniques, and Tools, 2nd editionHerlihy and Shavit, The Art of MultiProcessor ProgrammingAnanth Grama, Introduction to Parallel ComputingList of research papers and other material which will be the primary reference material will beavailable on course web page.

Prerequisites

Knowledge of "E0 255 Compiler Design" course content (especially on parallelization) will be veryuseful, but not absolutely necessary.Knowledge of microprocessor architecture and some basic understanding of parallel programmingmodels.

E0 361 (3:1) Topics in Database Systems Jayant R. Haritsa

Object-oriented Databases, Distributed and Parallel Databases, Multi-databases, Access Methods,Transaction Management, Query Processing, Deductive Databases, multimedia Databases, Real- TimeDatabases, Active Databases, Temporal Databases, Mobile Databases, Database Benchmarks, DatabaseSecurity, Data Mining and Data Warehousing.

References:

Readings in Database Systems edited by M. Stonebraker, Morgan Kaufmann, 2nd ed., 1994.Conference and Journal papers

E0 367 (3:1) Topics in Mobile Computing Technologies lalit

Wireless Technologies: Land Mobile Vs. Satellite Vs. In-building Communications Systems, CellularTelephony, Personal Communication Systems/Networks. Wireless Architectures for Mobile Computing.Applications. Wireless LANs, Wireless Networking, Hand-off, Media Access Methods, Mobile IP, Unicast andMulticast Communication, Wireless TCP, Security Issues. Mobile Computing Models, System-Level Support,Disconnected Operation, Mobility, Failure Recovery. Information Management, Broadcast, Caching,Querying Location Data. Location and Data Management for Mobile Computing, Hierarchical Schemes,Performance Evaluation. Case Studies.

References:

Current Literature from IEEE Transactions, Journals,and Conference Proceedings.Abdelsalam A. Helal et al, Any Time, Anywhere Computing : Mobile Computing Concepts andTechnology, Kluwer International Series in Engineering and Computer Science, 1999.Evaggelia Pitoura and Geaorge Samaras, Data Management for Mobile Computing, KluwerInternational Series on Advances in Database Management,October 1997.

Prerequisites

Consent of the Instructor

E0 370 (3:1) Statistical Learning Theory Shivani Agarwal


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Theoretical foundations of modern machine learning. Kernel methods: support vector machines. Ensemblemethods: boosting. Generalization analysis: VC-dimension bounds, covering numbers, margin analysis,Rademacher averages, algorithmic stability. Statistical consistency analysis. PAC learning. Online learningand regret bounds. Selected additional topics of current interest.

References:

Devroye, L, Gyorfi L, and Lugosi G, A Probabilistic Theory of Pattern Recognition. Springer, 1996.Anthony M, and Bartlett P L, Neural Network Learning: Theoretical Foundations. CambridgeUniversity Press, 1999.Vapnik V N, Statistical Learning Theory. Wiley-Interscience, 1998.Current literature.

Prerequisites

A strong foundation in probability and statistics, and some previous exposure to machine learning.Some background in linear algebra and optimization will be helpful.

E0 371 (3:1) Topics in Machine Learning Shivani Agarwal

Selected topics of current interest in machine learning and statistical learning theory. Examples includelearning and regularization on graphs, structured prediction, learning of sparse models, and low-rankmatrix approximations. Other topics may be selected based on class interest. This course will be based onseminars and research projects.

References:

Textbooks: Current literature, including (but not limited to) recent proceedings of COLT, ICML andNIPS conferences.

Prerequisites

Consent of the instructor.

E0 373 (3:1) Topological Methods for Visualization Vijay Natarajan

Topological methods for analyzing scientific data; efficient combinatorial algorithms in Morse theory;topological data structures including contour trees, Reeb graphs, Morse-Smale complexes, Jacobi sets, andalpha shapes; robustness and application to sampled data from simulations and experiments; multi-scalerepresentations for data analysis and feature extraction; application to data exploration and visualization.

References:

Textbooks: Course material will consist of current literature and lecture notes.

Prerequisites

Basic familiarity with fundamental algorithms and data structures is desirable (E0 225 or E0 251).Familiarity with the basics of scientific visualization will be useful but not essential. Interestedstudents with a non-CS background may also register for the course after consent of instructor.

E0 374 (3:1) Topics in Combinatorial Geometry Sathish Govindarajan

Fundamental Theorems: Radon's theorem, Helly's theorem. Geometric graphs: Proximity graphs, geometricresults on planar graphs. Geometric incidences: Incidence bounds using cuttings technique, crossinglemma. Distance based problems: Bounds on repeated distances and distinct distances. Epsilon Nets:Epsilon Net theorem using random sampling and discrepency theory, epsilon nets for simple geometricspaces, weak epsilon nets.

References:

Janos Pach and Pankaj K. Agarwal: Combinatorial Geometry, Wiley, 1st edition, 1995.J. Matousek: Lectures on Discrete Geometry, Springer-Verlag, 1st edition, 2002.Current literature

Prerequisites

The registrants should have preferably completed the "Design and Analysis of Algorithms" or"Discrete Structures" course.

E0 375 (3:1) Modern Applications of Automata Theory Deepak D Souza / priti

Verification using classical automata, Automata-theoretic techniques for shape analysis, Reachability inpushdown systems. Automata over distributed alphabets, and Message sequence charts. Automata overtrees, XML data / Regular tree language compression, Automata on nested words. Automata and logics


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over signals, Automata-based techniques for timed logics, Approximate regular behaviour of hybridautomata.

References:

Wolfgang Thomas: Applied Automata Theory (Electronic notes, RWTH Aachen)Current Literature

Prerequisites

A course on Automata Theory equivalent to E0 222.

E0 376 ( 3:1) Information Theory and Statistical InferenceAmbedkar Dukkipati /

Rajesh Sundaresan

Probability and basic information theory, universal data compression, I-projections and iterative algorithmsfor estimation with applications to statistics, large deviations and hypothesis testing, probabilities on metricspaces and information topology, Kolmogorov complexity, Applications of IT to other areas such as ergodictheory, gambling, biology.

References:

Information theory and Statistics: A Tutorial by I. Csisz_ar and P. Shields, Now Publications, 2008.Elements of Information Theory, by T. M. Cover and J. A. Thomas, John Wiley and Sons, 2ndedition, 2006.Information and Distribution: Occam's Razor in Action by P. Harremoes and A. Dukkipati, (inpreparation) 2008.Coding Theorems of Classical and Quantum Information theory by K. R. Parthasarathy, TRIMpublication, 2007.Information Theory, Inference, and Learning Algorithms by D.J.C. MacKay, Cambridge UniversityPress, 2003.

Prerequisites

Basic probability theory or consent of instructor.

E0 391 (3:1) Algebra and Computation T. KAVITHA

Preliminaries, polynomials, factorization of polynomials, Finite Fields, Berlekamp's algorithm, Hensel'slifting, LLL algorithm, applications to error correcting codes, the turnpike problem, some group theory,special cases of graph isomorphism, algorithms for primality testing.

References:

Joachim von zur Gathen and Jürgen Gerhard: Modern Computer AlgebraRelevant research papers and online notes.

E0 392 (2:0) Models and Algorithms for modern data kannan

Representing processing data as high-dimensional points, Random graphs and other random models,Probability Concentration phenomena, Eigen Values, Eigen vectors, Singular Value Decomposition andAlgorithmic applications, Massive Matrix Computations using randomized algorithms, Learning Algorithms,Optimization.

References:

Current Literature

Prerequisites

A solid undergrad background in Calculus, Linear Algebra, Probability and exposure to Algorithms.

E0 393 (3:1) Graph Theory and Combinatorics L. Sunil Chandran

Graph Theory: Connectivity, Matchings, Hamiltonian Cycles, Coloring Problems; Network flows, specialclasses of graphs. Introduction to Graph Minor theory. Combinatorics: Basic Combinatorial Numbers,Recurrence Relations, Inclusion-Exclusion Principle, Introduction to Polya Theory. Probabilistic Method inGraph theory: Basic Method, Expectation, Chernoff bound, Lovasz Local Lemma.

References:

J. H. Van Lint, R. M. Wilson, A Course in Combinatorics, Cambridge University Press, 1993.N. Alon and J. Spenser, "Probabilistic Methods", John Wiley and Sons, 2nd edition, 2000.R. Diestel, "Graph Theory", Springer-Verlag, 2nd edition, 2000.


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E0 394 (3:1)Performance Management of Internet

ApplicationsVarsha Apte

Part I: Performance AnalysisIntroduction to multi-tier application performance characteristics, Measurement-based performanceanalysis of distributed applications, Analytical Performance modeling of multi-tier applications, LayeredQueueing models (generic) Case studies of performance analysis of specific technologies (E.g. web server,virtual machines).Part II: Performance ManagementOverload control mechanims, QoS guarantee mechanisms, Dynamic resource provisioning mechanisms(e.g. in virtualized platforms), Power-aware performance management.

References:

Scaling for e-business: technologies, models, performance, and capacity planning, Daniel A.Menascé, Virgilio A. F. Almeida, Prentice-Hall, 2000.Papers:Woodside, Neilson, Petriu, Majumdar, The Stochastic Rendezvous Network Model for Performance ofSynchronous Client-Server-like Distributed Software, IEEE Trans. On Computers, January 1995 (vol.44 no. 1) pp. 20-34.Rolia and Sevcik, The Method of Layers, IEEE Transactions on Software Engineering, Volume 21 ,Issue 8 (August 1995), Pages: 689 - 700.John Dilley, Rich Friedrich, Tai Jin, Jerome Rolia, Web server performance measurement andmodeling techniques, Performance Evaluation, Volume 33 , Issue 1 (June 1998), Special issue ontools for performance evaluation, Pages: 5 - 26Paul Barford, Mark Crovella, Generating representative Web workloads for network and serverperformance evaluation, ACM SIGMETRICS Performance Evaluation Review, Volume 26, Issue 1(June 1998), Pages: 151 - 160.TF Abdelzaher, KG Shin, N Bhatti, Performance guarantees for web server endsystems: A control-theoretical approach, IEEE Transactions on Parallel and Distributed Systems, 2002.Comparison of the three CPU schedulers in Xen, L Cherkasova, D Gupta, A Vahdat - PerformanceEvaluation Review, 2007.B Urgaonkar, P Shenoy, A Chandra, P Goyal, Agile dynamic provisioning of multi-tier Internetapplications, ACM Transactions on Autonomous and Adaptive Systems (TAAS), Volume 3 , Issue 1(March 2008).Jeffrey S. Chase, Darrell C. Anderson, Prachi N. Thakar, Amin M. Vahdat, Ronald P. Doyle, Managingenergy and server resources in hosting centers, December 2001, SOSP '01: Proceedings of theeighteenth ACM symposium on Operating systems principles.

Prerequisites

It will be very useful to have a background in queuing systems (as provided in course E0 241, orany equivalent course from other departments). Undergraduate level background in OperatingSystems and Computer Networking will be assumed. Students should be comfortable with a broadrange of quantitative methods generally required in engineering.

E0 397 (3:1)Performance and Resource Management in

Virtualization and Cloud ComputingVarsha Apte

Application performance characteristics; Performance metrics, their fundamental behaviour with respect toallocated resources, offered load, etc; Overview of virtualization, Virtual Machines (e.g. Xen, KVM,VMware), Performance Isolation in virtual machines, Xen CPU Schedulers, schedulers in other VMs., Livemigration; Understanding energy as a resource, Energy consumption behaviour of server machines, howpower consumption can be controlled; Cloud Computing: overview, brief case studies , Dynamic andautonomic resource management in clouds, Resource allocation within one physical machine , Methodsbased on control theory, reinforcement learning, and other methods; Resource Management of a virtualizedcluster – specifically approaches for power usage reduction; Methods based on control theory,reinforcement learning, and other methods.

References:

The Definitive Guide To The Xen Hypervisor (Series - Prentice Hall Open Source SoftwareDevelopment) by David ChisnallRunning Xen: A Hands-on Guide To The Art Of Virtualization by Jeanna Matthews, Eli M. Dow, ToddDeshane. Prentice Hall.

Prerequisites

Undergraduate level background in Operating Systems and Computer Networking will be assumed.

E1 213 (3:1) Pattern Recognition and Neural Networks P. S. Sastry

Introduction to pattern recognition, Bayesian decision theory, supervised learning from data, parametric


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and non parametric estimation of density functions, Bayes and nearest neighbor classifiers, introduction tostatistical learning theory, empirical risk minimization, discriminant functions, learning linear discriminantfunctions, Perceptron, linear least squares regression, LMS algorithm, artificial neural networks for patternclassification and function learning, multilayer feed forward networks, backpropagation, RBF networks,support vector machines, kernel based methods, feature selection and dimensionality reduction methods.

References:

R. O Dudo, P.E. Hart & D. G. Stork, Pattern Classification John Wiley & sons, 2002.C.M Bishop, Neural Network & Pattern Recognition, Oxford University Press(Indian Edition) 2003.

Prerequisites

Knowledge of Probability theory.

E1 216 (3:1) Computer Vision Venu Madhav Govindu

This course will present a broad, introductory survey intended to develop familiarity with the approaches tomodeling and solving problems in computer vision. Mathematical modeling and algorithmic solutions forvision tasks will be emphasized. Image formation: camera geometry, radiometry, colour; Image features :points, lines, edges, contours, texture; Shape: object geometry, stereo, shape from cues; Motion:calibration, registration, Multiview geometry, optical Flow; approaches to grouping and segmentation;representation and methods for object recognition; applications.

References:

Computer Vision: A Modern Approach by David Forsyth and Jean Ponce, Prentice-Hall India, 2003.Multiple View Geometry in Computer Vision by R. Hartley and A. Zisserman, Second Edition,Cambridge University Press, 2004.Current literature.

E1 222 (3:0) Stochastic Models and Applications P. S. Sastry

Probability spaces, conditional probability, independence, random variables, distribution functions, multiplerandom variables and joint distributions. Expectations, moments, characteristic functions and momentgenerating functions, sequence of random variables and convergence concepts. Law of large numbers,central limit theorem, stochastic processes, Markov chains, stationary distribution of Markov chains,Poisson and birth and death processes.

References:

S. M. Ross, Introduction to Probability Models, (6th Edition), Academic Press and Harcourt Asia,2000Hoel, P. G., Port, S. C., and Stone, C. J., Introduction to Probability Theory, Indian Edition, UniversalBook Stall, New Delhi, 1998.Hoel, P. G., Port, S. C., and Stone, C. J., Introduction to Stochastic Process, Indian Edition,Universal Book Stall, New Delhi, 1981.

E1 241 (3:0) Dynamics of Linear Systems Vinod John

Background material on matrix algebra, differential equations. Representation of dynamic systems,equilibrium points and linearization. Natural and forced response of state equations, state spacedescriptions, canonical realizations. Observability and controllability, minimal realization. Linear statevariable feedback, stabilization, modal controllability, Jordan form, functions of matrices, pole-placement,Lyapunov matrix equations. Asymptotic observers, compensator design, and separation principle.Preliminary quadratic regulator theory.

References:

Chi-Tsong Chen, Linear Systems Theory and Design, HBJ 1984.Kailath T., Linear System Theory, Prentice Hall, 1980.

E1 244 (3:0) Detection and Estimation Theory Chandra R Murthy

Hypothesis testing, Neyman Pearson theorem, LRT and GLRT, UMP test, multiple-decision problem,detection of deterministic and random signals in Gaussian noise, detection in non-Gaussian noise.Sequential detection. Parameter estimation: unbiasedness, consistency, Cramer-Rao bound, sufficientstatistics, Rao-Blackwell theorem, best linear unbiased estimation, maximum likelihood estimation, methodof moments. Bayesian estimation: MMSE and MAP estimators, Wiener filter, Kalman filter, Levinson-Durbinand innovation algorithms.

References:

H. V. Poor, An Introduction to Signal Detection and Estimation, Springer-Verlag, 1994.


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E1 246 (3:1) Natural Language Understanding C.E. Veni Madhavan

Syntax: syntactic processing; linguistics; parts-of-speech; grammar and parsing; ambiguity resolution;tree adjoint grammars. Semantics: semantic interpretation; word sense disambiguation; logical form;scoping noun phrases; anaphora resolution. Pragmatics: context and world knowledge; knowledgerepresentation and reasoning;local discourse context and reference; discourse structure; semantic web;dialogue; natural language understanding and generation. Cognitive aspects: mental models, languageacquisition, language and thought; theories of verbal field cognition. Applications: text summarization,machine translation, sentiment analysis, perception evaluation, cognitive assistive systems; NLP tool-kitsaugmentation.

References:

Allen J, Natural language understanding, Pearson Education, 1995, 2003.Jurafsky D, and Martin J H, Speech and language processing: an introduction to natural languageprocessing, computational linguistics and speech recognition, Pearson Education, 2000, 2003.Posner M I, Foundations of Cognitive Science, MIT Press, 1998.Research Literature.

Prerequisites

Familiarity with programming (optionally including scripting languages); data structures, algorithmsand discrete structures; reasonable knowledge of English language.

E1 247 (2:1) Incremental Motion ControlN. S. Dinesh and J. E.

Diwakar

Introduction to various incremental motion systems. Principles of operation and classification of varioustypes of steeper motors, control and drive circuits. Improved control and drive techniques in open andclosed loop. Use of DC motors in incremental motion systems and related control techniques.

References:

Kuo BC, Step Motors and Control Systems, SRL Publishing Co., Illinois, 1979Proceedings of Annual Symposium on Incremental Motion Control Systems and Devices, from 1974onwards published by IMCSS ChampainSrinivasan, M. P., Stepping Motors: Lecture Notes CEDT/IISc, Publication 1983

E1 251 (3:0) Linear and Nonlinear Optimization K. R. Ramakrishnan

Necessary and sufficient conditions for optima; convex analysis; unconstrained optimization; descentmethods; steepest descent, Newton’s method, quasi Newton methods, conjugate direction methods;constrained optimization; Kuhn-Tucker conditions, quadratic programming problems; algorithms forconstrained optimization; gradient projection method, penalty and barrier function methods, linearprogramming, simplex methods; duality in optimization, duals of linear and quadratic programmingproblems.

References:

J.Luenberger D.G. Introduction to Linear and Nonlinear Programming, 2nd edition, Addison Wesley,1984.Fletcher. R: Practical methods of Optimization John Wiley, 1980.

E1 254 (3:1) Game Theory Y. Narahari

Introduction: rationality, intelligence, common knowledge, von Neumann - Morgenstern utilities;Noncooperative Game Theory: strategic form games, dominant strategy equilibria, pure strategy nashequilibrium, mixed strategy Nash equilibrium, existence of Nash equilibrium, computation of Nashequilibrium, matrix games, minimax theorem, extensive form games, subgame perfect equilibrium, gameswith incomplete information, Bayesian games. Mechanism Design: Social choice functions and properties,incentive compatibility, revelation theorem, Gibbard-Satterthwaite Theorem, Arrow's impossibility theorem,Vickrey-Clarke-Groves mechanisms, dAGVA mechanisms, Revenue equivalence theorem, optimal auctions.Cooperative Game Theory: Correlated equilibrium, two person bargaining problem, coalitional games, Thecore, The Shapley value, other solution concepts in cooperative game theory.

References:

Roger B. Myerson, Game Theory: Analysis of Conflict, Harvard University Press, September 1997.Martin J. Osborne, An Introduction to Game Theory, Oxford University Press, 2003.Y. Narahari, Dinesh Garg, Ramasuri Narayanam, Hastagiri Prakash. Game Theoretic Problems inNetwork Economics and Mechanism Design Solutions. Springer, 2009.


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E1 313 (3:1) Topics in Pattern Recognition M. Narasimha Murty

Foundations of pattern recognition. Soft computing paradigms for classification and clustering.Knowledge-based clustering. Association rules and frequent itemsets for pattern recognition. Large-scalepattern recognition.

References:

R. O. Duda, P. E. Hart, and D.G. Stork, Pattern Classification, John Wiley & Sons (Asia), Singapore,2002Recent Literature.

E1 335 (3:1) Cognition and Machine Intelligence C.E. Veni Madhavan

Biological cerses computational dichotomy, critical computer - anatomy of neocortex, 100 steps at 5 msecrule, symbolic architecture, connectionist approach, multi-sensory-motor information, hierarchical,network, pyramidal models, spatio-temporal pattern matching, pattern representation and storage,invariant representations, sequences of sequences, autoassociative, content addressable memory retrieval,memory prediction paradigm, domains: language acquiaition, vision and attention, mental models, designand development of thought experiments and simulation.

References:

Posner M I, Foundations of Cognitive Science, The MIT Press, 1993.Books and Survey Articles by: M. Minsky, A. Newell, H.A. Simon, D.E. Rumelhart, T. Sejnowski, J.Barwise, N. Chomsky, S. Pinker, V.S. Ramachandran and others

E1 354 (3:1) Topics in Game Theory Y. Narahari

Foundational results in game theory and mechanism design: Nash's existence theorem, Arrow'simpossibility theorem, Gibbard Satterthwaite theorem, etc.; Selected topics in repeated games,evolutionary games, dynamic games, and stochastic games; Selected topics at the interface between gametheory, mechanism design, and machine learning; Selected topics in algorithmic game theory; Modernapplications of game theory and mechanism design: incentive compatible learning, social network analysis,etc.

References:

Roger B. Myerson, Game Theory: Analysis of Conflict, Harvard University Press, September 1997.Rakesh V. Vohra: Advanced Mathematical Economics. Routledge, New York, NY, 2005.Andreu Mas-Colell, Michael D. Whinston, and Jerry R. Green: Microeconomic Theory. OxfordUniversity Press, New York, 1995.Current Literature

Prerequisites

Elementary knowledge of linear algebra, linear programming, algorithms, game theory is useful forthis course.

E1 395 (3:0)Topics in Stochastic Control and Reinforcement

LearningShalabh Bhatnagar

Markov decision processes, finite horizon models, infinite horizon models under discounted and long-runaverage cost criteria, classical solution techniques -- policy iteration, value iteration, problems with perfectand imperfect state information. Reinforcement learning, solution algorithms -- Q-learning, TD(lambda),actor-critic algorithms.

References:

D.P.Bertsekas, Dynamic Programming and Optimal Control, Vol.I and II, Athena Scientific, 2005.D.P.Bertsekas and J.N.Tsitsiklis, Neuro-Dynamic Programming, Athena Scientific, 1996.R.S.Sutton and A.G.Barto, Reinforcement Learning: An Introduction, MIT Press, 1998.Selected Research Papers.

Prerequisites

A course on probability theory and stochastic processes. Knowledge of nonlinear programming isdesirable.

E1 396 (3:0) Topics in Stochastic Approximation Algorithms Shalabh Bhatnagar

Introduction to Stochastic approximation algorithms, ordinary differential equation based convergenceanalysis, stability of iterates, multi-timescale stochastic approximation, asynchronous update algorithms,gradient search based techniques, topics in stochastic control, infinite horizon discounted and long run


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average cost criteria, algorithms for reinforcement learning.

References:

H.J.Kushner and G.Yin, Stochastic approximation and recursive algorithms and applications (2ndedition), Springer Verlag, New York, 2003.A.Benveniste, M.Metiview and P.Priouret, Adaptive algorithms and stochastic approximation,Springer-Verlag,1990.V.S.Borkar,Stochastic Approximation: A Dynamical Systems Viewpoint, Hindustan Book Agency,2008.D.P.Bertsekas and J.N.Tsitsiklis, Neuro-dynamic programming, Athena Scientific, 1996.Relevant research papers

Prerequisites

A basic course on probability theory and stochastic processes


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Computer Science and Automation - IISc

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