Class Topics

Topic		Slides		Required Reading		Optional Reading
Introduction		ppt pdf		Ch.1 from I2ML
Supervised Learning		ppt pdf		Ch.2 from I2ML
Linear Classifiers I		ppt pdf		Andrew Ng's notes: Disc. Learning Algos: Logistic Regression etc. Gen. Learning Algos: LDA etc.
Linear Classifiers II		ppt pdf		Naive Bayes Text Classif. Discr. vs. Gen. Classifiers
Decision Trees		ppt pdf		Read any one of the following: Ch. 9.1-9.4 from I2ML Ch. 3 from ML Ch. 8.2-8.4 from DHS
Non-Parametric Classifiers		ppt pdf		Read any one of the following: Ch. 8 from I2ML Ch. 4.4-4.6 from DHS Ch. 4.4-4.6 from DHS
Evaluation		ppt pdf
Support Vector Machines		ppt pdf		Read any one of the following: Andrew Ng's Notes Burges Tutorial ICML tutorial
Bias-Variance Tradeoff		ppt pdf
Bayesian Learning		ppt pdf
Neural Networks		pdf
Clustering I		ppt pdf		Read material from any one of I2ML or HMS: Sec. 9.6, 6.4, 7.3.2, 7.4 and 8.4 from HMS Ch. 7 from I2ML
Clustering II		ppt pdf		Andrew Ng's notes: Mixture of Gaussians and EM EM derivation and proof		Optional reading: Visualizing of Navigation Patterns of a web site using model based clustering by Cadez et al.
Spectral Clustering		ppt pdf		"Co-clustering documents and words using bipartite spectral graph clustering" by Inderjit Dhillon "Spectral Analysis of data" by Azar et al. "Segmentation using Eigenvectors: a unifying view" by Yair Weiss
Bayes nets: Representation		ppt pdf
Bayes nets: Inference		ppt pdf		Please read the following: Chapter 7 from BNB Sections 8.1,8.2,8.3 and 8.7 from BNB
Bayes nets: Learning		ppt pdf		Please read Chapter 15 from BNB		Optional reading: Tutorial on Learning with Bayes nets
Hidden Markov Models		ppt pdf		HMM notes
Reinforcement Learning		ppt pdf		Read any one of: ML ch. 13 Reinforcement Learning: A Survey
Wrap up		ppt pdf

Key:

• I2ML: Introduction to Machine Learning by Ethem Alpaydin
• ML: Machine Learning by Tom M. Mitchell
• DHS: Pattern Classification (2nd Edition) by Richard O. Duda, Peter E. Hart, David G. Stork
• HTF: The Elements of Statistical Learning by T. Hastie, R. Tibshirani, J. H. Friedman
• HMS: Principles of Data Mining by Hand, Mannila, and Smyth (MIT Press)
• BNB: Bayesian Networks and Beyond by Daphne Koller and Nir Friedman