Computer Science 246/446


Spring 2006

Homework 6
Due Thursday April 6 in class Due Friday April 7 5pm
Homework 5
Due Tuesday March 7 in class

Kearns and Vazirani exercises 3.1, 3.3, 3.4

Homework 4
Due Thursday Feb 16 in class

Convert the following simple belief networks into the factor graph representation, and run the polytree (sum-product) algorithm by hand, computing expressions for each message in terms of the parameters of the original belief net, and showing that the final result gives the desired probability:

Due Friday Feb 17 5pm

Implement EM fitting of a mixture of gaussians on the two-dimensional data set hw4.dat. You should (at a minimum) try different numbers of mixtures, as well as tied vs. separate covariance matrices for each gaussian. Which model seems to fit the data best?

Homework 3
Due Friday Feb 10 5pm

Implement the Decision Tree algorithm described in class, including the MDL criterion. Test it using the following dataset: voting2.dat. Turn in your code along with a report describing how the algorithm worked, and any observations about the types of trees it learned. You should try varying (at least) the amount of training data (the training/test split is up to you), whether MDL is used, and how the description length is weighted. You may use any programming language.

Homework 2
Due Tuesday 2/6 in class

Homework 1
Due Thursday 1/26 in class

Wasserman ch. 1 ex. 10, 11, 12, 13, 14, 15, 17; ch. 2 ex. 14; ch. 3 ex. 4, 5, 14

gildea @ cs rochester edu
January 23, 2007