Topic List and Schedule

2/23		Course Overview & Introduction
3/2		Data Cleansing and Preparation  (Kantard, Ch. 2)	HW-1: Data Preparation: Moving Averages
3/9		Data Dimensionality Reduction - PCA, LDA, LSA etc. (Alpaydin, Ch. 6)	HW-2: Data Reduction: Entropy Measure, ChiMerge
3/16		Supervised Learning - PAC, VC-Dimension etc.  (Alpaydin, Ch. 2)	HW-3: PCA & LDA (Male, Female)
3/23		Concept Learning (Mitchell, Ch. 2)
3/30		Bayesian Decision Theory (I) (Alpaydin, Ch. 3; Mitchell, Ch. 6)
4/6		Bayesian Decision Theory (II) (Alpaydin, Ch. 3; Mitchell, Ch. 6)
4/13		Parametric Methods - Bias and Variance of the Estimator (Alpaydin, Ch.4)
4/20		Midterm
4/27		Parametric Methods - Bias and Variance of the Estimator
5/4		Break
5/11		Multivariate Models (Alpaydin, Ch. 5)
5/18		Multivariate Models (Alpaydin, Ch. 5)
5/25		Nonparametric Methods: Decision Trees (Alpaydin, Ch. 9, Mitchell, Ch. 6)
6/1		Association Rules (Kantard, Ch. 8; Han and Kamber, Ch. 9)
6/8		Talk at NCNU
6/15		Linear Discrimination [Alpaydin's Original Slides] (Alpaydin, Ch. 10)
6/20		Final Exam (Tuesday)

Textbook: 

1.

Ethem Alpaydin, Introduction to Machine Learning, MIT Press, 2004

Major References: 

1.		Tom M. Mitchell, Machine Learning, McGraw-Hill, 1997.
2.		Mehmed M. Kantard, Data Mining: Concepts, Models, Methods and Algorithms, Wiley-IEEE Press, 2002.
3.		T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning; Data Mining, Inference, and Prediction, Springer-Verlag, 2001.
4.		Richard O. Duda, Peter E. Hart, David G. Stork, Pattern Classification (Second Edition), Wiley 2000

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