No credit for late homework!
 Homework 9, due in class 4/30
 Final review problems handed out in class.
 Homework 8, due Mon 4/21 3:25pm by turn_in script

Implement EM to train an HMM for whichever dataset
you used for assignment 7.
The observation probs should be as in assignment 10:
either gaussian, or two discrete distributions conditionally
independent given the hidden state.
Does the HMM model the data better than the original
nonsequence model? What is the best number of states?
 Homework 7, due Mon 4/7 3:25pm by turn_in script

Implement EM fitting of a mixture of gaussians on the twodimensional
data set points.dat. You should
try different numbers of mixtures, as well as tied vs. separate
covariance matrices for each gaussian.
OR
Implement EM fitting of the aspect model on the
discrete data set pairs.dat.
You should try different numbers of mixtures.
IN EITHER CASE
Use the final 1/10 of the data for dev.
Plot likelihood on train and dev vs iteration
for different numbers of mixtures.
 Homework 6, due Wed 3/19 in class
 review problems handed out 3/5
 Homework 5, due Wed 2/26 in class

 Bishop 6.2, 6.8, 6.9
 Bishop 7.2, 7.7
 Homework 5, due Wed 2/19 3:25pm by turn_in script

Implement SVMs with SGD for the voting dataset,
and compare results with the previous assignment.
Use the dev set to experiment with different values of the capacity parameter
C and the learning rate.
 Homework 4, due Wed 2/12 3:25pm by turn_in script

Implement the perceptron algorithm for the voting dataset,
and compare results with the previous assignment.
 Homework 3, due Wed 2/5 3:25pm by turn_in script

Implement linear regression
for the voting dataset using Python.
Split the 435 examples into 348 train / 45 dev / 42 test and report your
classification accuracy on the test set.
 Homework 2, due in class 1/29
 Available on NB.
 Homework 1, due in class 1/22
 Available on NB.
gildea @ cs rochester edu
April 23, 2014
