No credit for late homework!
 Homework 9, due Wed 4/29 in class
 Review problems handed out in class
 Homework 8, due Wed 4/15 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 7:
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 Wed 4/1 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 3/4 in class
 Review problems handed out on 2/25
 Homework 5, due Mon 2/23 in class

 Bishop 6.2, 6.8, 6.9
 Bishop 7.2, 7.7
 Homework 4, due Mon 2/16 3:25pm by turn_in script

Implement perceptron and SVM for the adult income dataset.
Report how performance on dev varies as a function of C,
and how perceptron and SVM compare.
 Homework 3, due Mon 2/2 3:25pm by turn_in script

Implement naive bayes with Dirichlet smoothing
for the adult income dataset.
Plot classification error on the dev set as a function of alpha,
and report the performance of the best alpha on the test set.
 Homework 2, due Mon 1/26 3:25pm by turn_in script

Implement linear regression
for the adult income dataset using Python.
Data available in /u/cs246/data/adult and /u/cs446/data/adult.
Take the sign of the output of linear regression as the predicted class
label in order to compute the accuracy of your classifier.
 Homework 1, due in class 1/21
 pdf
gildea @ cs rochester edu
April 24, 2015
