Lecture plan ECS-074 Pattern Recognition
Unit No.	Topic	Lecture
Unit I	Introduction: Basics of pattern recognition,	Lecture 1
	Design principles of pattern recognition system,	Lecture 2
	Learning and adaptation,	Lecture 3
	Pattern recognition approaches,	Lecture 4
	Mathematical foundations – Linear algebra,	Lecture 5
	Probability Theory, Expectation,	Lecture 6
	mean and covariance, Normal distribution,	Lecture 7
	multivariate normal densities,	Lecture 8
	Chi squared test.	Lecture 9
Unit II	Statistical Patten Recognition	Lecture 10
	Bayesian Decision Theory,	Lecture 11
	Classifiers,	Lecture 12
	Normal density and discriminant functions	Lecture 13
Unit III	Parameter estimation methods:	Lecture 14
	Maximum-Likelihood estimation,	Lecture 15
	Bayesian Parameter estimation,	Lecture 16
	Dimension reduction methods - Principal Component Analysis (PCA),	Lecture 17
	Fisher Linear discriminant analysis,	Lecture 18
	Expectation-maximization (EM),	Lecture 19
	Gaussian mixture models.	Lecture 20
	Hidden Markov Models(HMM),	Lecture 21
Unit IV	Nonparametric Techniques: Density Estimation,	Lecture 22
	Parzen Windows,	Lecture 23
	K-Nearest Neighbor Estimation,	Lecture 24
	Nearest Neighbor Rule,	Lecture 25
	Fuzzy classification.	Lecture 26
Unit V	Unsupervised Learning & Clustering: Criterion functions for clustering,	Lecture 27
	Clustering Techniques: Iterative square - error partitional clustering	Lecture 28
	– K means clustering,	Lecture 29
	agglomerative hierarchical clustering,	Lecture 30
	Cluster validation	Lecture 31
	Revision	Lecture 32

References:

1. Richard O. Duda, Peter E. Hart and David G. Stork, “Pattern Classification”, 2nd Edition, John Wiley, 2006.

2. C. M. Bishop, “Pattern Recognition and Machine Learning”, Springer, 2009.

3. S. Theodoridis and K. Koutroumbas, “Pattern Recognition”, 4th Edition, Academic Press, 2009.