Reading assignments
Assignments are to be read before lecture on the day they are due.
Assigned due   chapters      topic
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 9/22    9/27  1 and 2      Introduction to ML
 9/22    9/27  append A     Probability
 9/27	 10/4  3 and 4	    Bayesian learning (3.7 will be covered later)
 10/5	 10/9  5	    Multidimensional Bayesian learning
 10/5	 10/11 9	    Decision trees
 10/6    10/11 10.3-10.7    Logistic Regression
10/10	 10/13 11.1-11.5    ANN - perceptrons 
10/10	 10/16 11.6-11.15   ANN - Backprop
10/16	 10/20 10	    Linear Discrimination and SVM
10/20	 10/25 8	    Non parametric learning (nearest neighbor)
10/24	 10/28 Schapire	    MSRI boosting overview paper
10/24	 10/31 15	    Ensemble methods
10/30	 11/3  7	    clustering
10/30	 11/3  A Note on EM (see link under additional readings)
10/30	 11/6  13	    Hidden Markov Models
11/3	 11/10 Avrim Blum   On-line learning survey "On-Line Algorithms in Machine Learning"
11/3	 11/10 Littlestone/Warmuth  "The Weighted Majority Algorithm" (sections 1 and 2)
11/17	 11/22 6		    feature selection
11/17	 11/22 section 1 of: "An introduction to variable and feature selection"  by Guyon and Elisseeff