Reading assignments
Assignments are to be read before lecture on the day they are due.
Assigned due   chapters      topic
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 9/23    9/28  1 and 2      Introduction to ML
 9/23    9/28  append A     Probability
 9/26    9/28  11.2-11.4    Perceptron 
 9/26    9/30  10.3-10.7    Logistic regression 
 9/26    9/30  6.6          Linear discriminant analysis
 9/26    none  paper        Introduction to Variable and Feature Selection
10/5	 10/10 3	    Bayesian Decision theory
10/14    none  paper	    Read either Charniak's Bayesian networks without tears, or the
                            Bayesian Network sections in Chapter 14 of Russell and Norvig's AI book
                            Heckerman's paper is a heftier alternative.
10/14    10/19 9            Decision Trees
10/14	 10/21 4.1-4.5	    Parametric Bayesian Learning
10/19	 10/26 11	    Artificial Neural Networks (assigned in lecture, added here 10/24)
10/24	 10/28 8	    Instance Based (non-parametric) methods 
10/28	 10/31 10.9	    Support vector machines
11/1	 11/4  15.1-15.5    Boosting
11/1	 11/7  paper	    Robert Schapire's MSRI boosting overview
11/9	 11/16 7	    clustering and EM (see Bilmes tutorial for more on EM)
11/18	 11/21 handout	    A note on EM