E1 Intelligent System and Automation

E1 213 (JAN) 3:1

Pattern Recognition and Neural Networks

Introduction to pattern recognition, Bayesian decision theory, supervised learning from data, parametric and non parametric estimation of density functions, Bayes and nearest neighbor classifiers, introduction to statistical learning theory, empirical risk minimization, discriminant functions, learning linear discriminant functions. Perceptron, linear least squares regression, LMS algorithm, artificial neural networks for pattern classification and function learning, multilayer feed forward networks, backpropagation, RBF networks, support vector machines, kernel based methods, feature selection and dimensionality reduction methods.

P S Sastry

Dudo, R.O., Hart, P.E., and Stork, D.G., Pattern Classification, John Wiley and Sons, 2002.

Bishop, C.M., Neural Network and Pattern Recognition, Oxford Univ. Press, Indian Edn, 2003.

Prerequisite: Knowledge of Probability theory

E1 216 (JAN) 3:1

Computer Vision

This course will present a broad, introductory survey intended to develop familiarity with the approaches to modeling and solving problems in computer vision. Mathematical modeling and algorithmic solutions for vision tasks will be emphasised. 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.

Venu Madhav Govindu

Forsyth, D., and Ponce, J., Computer Vision: A Modern Approach, Prentice-Hall India, 2003.

Hartley, R., and Zisserman, A., Multiple View Geometry in Computer Vision, Second Edn, Cambridge University Press, 2004.

Current literature

E1 222 (AUG) 3:0

Stochastic Models and Applications

Probability spaces, conditional probability, independence, random variables, distribution functions, multiple random variables and joint distributions. Expectations, moments, characteristic functions and moment generating 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.

P S Sastry

Ross, S.M., Introduction to Probability Models, Sixth Edn, Academic Press and Hardcourt Asia, 2000.

Hoel, P.G., Port, S.C., and Stone, C.J., Introduction to Probability Theory, Indian Edn, Universal Book Stall, New Delhi, 1998.

Hoel, P.G., Port, S.C., and Stone, C.J., Introduction to Stochastic Process, Indian Edn, Universal Book Stall, New Delhi, 1981.

E1 241 (AUG) 3:0

Dynamics of Linear Systems

Representation of dynamic systems, linear operators, state space descriptions, equilibrium points and linearization, natural and forced response of state equations, modal decomposition, stability, Lyapunov matrix equations, canonical realizations, observability and controllability, system equivalence, linear state variable feedback, pole-placement, stabilization, asymptotic observers, separation principle, compensator design, discrete time modeling of continuous time systems.

Vinod John

Chi-Tsong Chen, Linear Systems: Theory and Design, HBJ, 1984.

Kailath, T., Linear System Theory, Prentice Hall, 1980.

E1 244 (JAN) 3:0

Detection and Estimation Theory

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, sufficient statistics, Rao-Blackwell theorem, best linear unbiased estimation, maximum likelihood estimation, method of moments. Bayesian estimation: MMSE and MAP estimators, Wiener filter, Kalman filter, Levinson-Durbin and innovation algorithms.

Chandra R Murthy

Kay, S.M., Fundamentals of Statistical Signal Processing, Vols.1&2, Prentice Hall, 1993 & 98.

E1 247 (AUG) 2:1

Incremental Motion Control

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

N S Dinesh and J E Diwakar

Kuo, B.C., Step Motors and Control Systems, SRL Publishing Co., Illinois, 1979.

Proceedings of Annual Symposium on Incremental Motion Control Systems and Devices, from 1974 onwards published by IMCSS Champain.

Srinivasan, M.P., Stepping Motors: Lecture Notes CEDT/IISc Publication, 1983.

E1 251 (AUG) 3:0

Linear and Nonlinear Optimization

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

K R Ramakrishnan

Luenberger, D.G., Introduction to Linear and Nonlinear Programming, Second Edn, Addison Wesley, 1984.

Fletcher, R., Practical methods of Optimization, John Wiley, 1980.

E1 254 (JAN) 3:1

Game Theory

Introduction: rationality, intelligence, common knowledge, von Neumann–Morgenstern utilities. Non-cooperative Game Theory: strategic form games, dominant strategy equilibria, pure strategy Nash equilibrium, mixed strategy Nash equilibrium, existence of Nash equilibrium, computation of Nash equilibrium, matrix games, minimax theorem, extensive form games, subgame perfect equilibrium, games with 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, The core, The Shapley value, other solution concepts in cooperative game theory.

Y Narahari

Myerson, R.B., Game Theory: Analysis of Conflict, Harvard Univ. Press, September 1997.

Osborne, M.J., An Introduction to Game Theory, Oxford University Press, 2003.

Narahari, Y., Garg, D., Narayanam, R., and Prakash, H., Game Theoretic Problems in Network

Economics and Mechanism Design Solutions, Springer, 2009.

E1 313 (AUG) 3:1

Topics in Pattern Recognition

Foundations of pattern recognition. Soft computing paradigms for classification and clustering. Knowledge-based clustering. Association rules and frequent item sets for pattern recognition. Large-scale pattern recognition.

M Narasimha Murty

Duda, R.O., Hart, P.E., and Stork, D.G., Pattern Classification, John Wiley and Sons, Singapore, 2002.

Recent Literature.

E1 335 (JAN) 3:1

Cognition and Machine Intelligence

Biological versus computational dichotomy. Cortical computer – anatomy of neocortex, 100 steps at 5 msec rule. 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. Auto-associative, content addressable memory retrieval. Memory prediction paradigm. Domains: language acquisition, vision and attention, mental models. Design and development of thought experiments and simulation.

C E Veni Madhavan

Posner, M.I. (ed.), Foundations of Cognitive Science, The MIT Press, 1993.

Books and Survey Articles by M Minsky, A Newell, HA Simon, DE Rumelhart, Sejnowski,

J Barwise, N Chomsky, S Pinker, VS Ramachandran and others.

E1 354 (AUG) 3:1

Topics in Game Theory

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

Y Narahari

Myerson, R.B., Game Theory: Analysis of Conflict, Harvard University Press, September 1997.

Vohra, R.V., Advanced Mathematical Economics, Routledge, NY, 2005.

Mas-Colell, A., Whinston, M.D., and Green, J.R., Microeconomic Theory, Oxford Univ. Press, NY,

1995.

Current Literature

Prerequisites: Elementary knowledge of linear algebra, linear programming, algorithms and game theory.

E1 395 (AUG) 3:0

Topics in Stochastic Control and Reinforcement Learning

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

Shalabh Bhatnagar

Bertsekas, D.P., Dynamic Programming and Optimal Control, Vols. I&II, Athena Scientific, 2005.

Bertsekas, D.P., and Tsitsiklis, J.N., Neuro-Dynamic Programming, Athena Scientific, 1996.

Sutton, R.S., and Barto, A.G., Reinforcement Learning: An Introduction, MIT Press, 1998.

Selected Research Papers.

Prerequisite: Probability theory and stochastic processes. Knowledge of nonlinear programming is desirable.