Topic List and Schedule

2/25		Course Overview & Introduction
3/3		Supervised Learning - PAC, VC-Dimension etc.  (Alpaydin, Ch. 2)
3/10		Data Cleansing and Preparation  (Kantard, Ch. 2)	HW-1: Data Preparation: Moving Averages Due: 3/17
3/17		Data Cleansing and Preparation (Contd.)	HW-2: Data Reduction: Entropy Measure, ChiMerge Due: 3/24
3/24		Data Dimensionality Reduction - PCA, LDA, LSA etc. (Alpaydin, Ch. 6)	HW-3: PCA & LDA (Male, Female) Due: 4/14
3/31		Concept Learning (Mitchell, Ch. 2)
4/7		Bayesian Decision Theory (Alpaydin, Ch. 3; Mitchell, Ch. 6)	HW-4: Bayesian Networks Due: 4/21
4/14		Bayesian Decision Theory (Contd.)
4/21		Midterm
4/28		Parametric Methods - Bias and Variance of the Estimator (Alpaydin, Ch.4)	HW-5: Parametric Classification Due: 5/12
5/5		Multivariate Models  (Alpaydin, Ch. 5)
5/12		Clustering  (Alpaydin, Ch. 7)
5/19		Nonparametric Methods: Decision Trees  (Alpaydin, Ch. 9, Mitchell, Ch. 6)
5/26		Nonparametric Methods: Decision Trees (Contd.)	HW-6: Document Clustering Due: 6/30
6/2		Association Rules (Kantard, Ch. 8; Han and Kamber, Ch. 9)
6/9
6/16		Final Exam
		Nonparametric Methods: Density/Function Estimation (Alpaydin, Ch. 8) Linear Discrimination - Kernel Methods, SVM etc. (Alpaydin, Ch. 10)

Major References: 

1.		Ethem Alpaydin, Introduction to Machine Learning, MIT Press, 2004
2.		Tom M. Mitchell, Machine Learning, McGraw-Hill, 1997.
3.		Mehmed M. Kantard, Data Mining: Concepts, Models, Methods and Algorithms, Wiley-IEEE Press, 2002.
4. .		T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning; Data Mining, Inference, and Prediction, Springer-Verlag, 2001.

Other References:

1.		Jiawei Han and Micheline Kamber, Data Mining: Concepts and Techniques, Morgan Kaufmann, 2001.
2.		Nello Christanini, John Shawe-Tayer, An Introduction to Support Vector Machines, Cambridge University Press 2000
3.		Michael Berthold and David J. Hand. Intelligent Data Analysis: An Introduction. Springer-Verlag, 2003.
4.		I. H. Witten and E. Frank, Data Mining, Morgan Kaufmann, 2000.
5.		Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach, Prentice-Hall, 2003.
6.		Nils J. Nilsson, Artificial Intelligence: A New Synthesis, Morgan Kaufmann, 1998.
7.		C. Borgelt and R. Kruse, Graphical Models: Methods for Data Analysis and Mining, John Wiley & Sons, 2002

Papers/Drafts:

1.		"Machine Learning and Data Mining," T. Mitchell, Communications of the ACM, Vol. 42, No. 11, November 1999.
2.		"A Gentle Tutorial of the EM Algorithm and its Application to Parameter Estimation for Gaussian Mixture and Hidden Markov Models," Jeff A. Bilmes, U.C. Berkeley TR-97-021
3.		Nils J. Nilsson, Introduction to Machine Learning, 1996