Computer Science 246/446

Homework

Spring 2007

Homework 9

Due Tuesday 5/1 in class

• Thrun et al., ch4 ex 1, 2, 4, 5

Homework 8

Due Tuesday 4/10 in class

• Bishop 4.18, Bishop 1995 7.9

Homework 7

Due Tuesday 3/20 in class

• Bishop 12.5, 12.7, K and V 3.1, 3.6

Homework 6

Due Tuesday 3/6 in class

• Bishop 9.11, 9.19, 11.13

Homework 5

Due Tuesday 2/27 before class

Implement EM fitting of a mixture of gaussians on the two-dimensional data set points.dat. You should 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 4

Due Tuesday 2/13 in class

• Bishop 5.1, 5,4, 5.7, 6.2, 6.11

Homework 3

Due Tuesday 2/6 in class

• Bishop 2.15, 4.10, 4.11
• Implement a naive bayes classifier with a Dirichlet prior to predict the party of a US representative from their voting record: voting2.dat. Experiment with different values for the prior parameters: what setting gives the highest classification rate?

Homework 2

Due Tuesday 1/30 in class

• Bayes nets: Prove that, given a network structure A->B->C, A is conditionally independent of C given B.
• Explaining away: Prove that, given a network structure A->B<-C, A is not independent of C given B, and is independent when marginalizing over B.
• Use Lagrange multipliers to derive the multinomial distribution over K possible outcomes that maximizes entropy.
• Bishop 1.32

Homework 1

Due Tuesday 1/23 in class

Bishop ex. 1.3, 1.5, 1.6, 1.11