Instructor:
Dan Gildea office hours Wed 12pm
TA: Mehdi Hafezi Manshadi office hours Tu/Th 56pm
Location: Tu/Th 11:05am12:20pm, CSB 601
Homeworks
Required text: Christopher M. Bishop, Pattern Recognition and Machine Learning.
The following are useful references in addition
to the reading material assigned for each class:
 Stuart Russell and Peter Norvig, Artificial Intelligence, A Modern Approach.
 Trevor Hastie, Robert Tibshirani, Jerome Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction.
 Dana Ballard, Natural Computation.
Readings not taken from Bishop are available in the box in the CS mailroom.
Syllabus
On  we will cover  which means that after class you will understand  if before class you have read 
1/17 
Probability Theory 
independence, bayes rule
 bishop 1.2 
1/22 
Information Theory 
entropy, kldistance, coding
 bishop 1.6 
1/24 
Probabilistic Inference 
priors on continuous variables
 bishop 2.2 
1/29 
Probabilistic Inference 
priors: bayesian reasoning, MAP
 bishop 3.4 
1/31 
Perceptron, Backpropagation 
the chain rule
 bishop 5.1, 5.2, 5.3 
2/5 
Support Vectors 
the kernel trick
 bishop 6.1, 6.2 
2/7 
Support Vectors 
the wolfe dual
 bishop 7.1 
2/12 
Probabilistic Inference 
polytree
 bishop 8.4 
2/14 
Expectation Maximization 
latent variable clustering
 bishop 9 
2/19 
Sampling 
Markov Chain Monte Carlo
 bishop 11.2 
2/21 
Sampling 
Gibbs Sampling
 bishop 11.3 
2/26 
Learning Theory 
probably approximately correct
 kearns&vazirani 1 
2/28 
Learning Theory 
VC dimension
 kearns&vazirani 2, 3 
3/4 
Eigenvectors 
least squares, PCA
 bishop 12.1 
3/6 
Independent Component Analysis 
source separation
 bishop 12.4.1 
3/18 
Review 
 
3/20 
Midterm 
 
3/25 
Midterm Solutions 


3/27 
Logistic Regression 
maximum entropy
 bishop 4.3 
4/1 
Optimization 
conjugate gradient
 bishop (1995) 274282 
4/3 
Hidden Markov Models 
forwardbackward
 bishop 13.2 
4/8 
Particle Filters 
slam
 Thrun et al. ch 4 
4/10 
Reinforcement Learning 
qlearning
 ballard 11 
4/15 
Reinforcement Learning 
partial observability
 Thrun et al. ch 15, 16 
4/17 
Games 
nash equilibrium
 morris 115131 
4/22 
Games 
learning to cooperate
 
4/24 
Something Fun 
 
4/29 
Review 
come to class with questions!
 
Final Exam: Friday May 9, 8:3011:30am.
Grading
 Final exam: 35%
 Homeworks: 35%
 Midterm: 25%
 Class participation: 5%
gildea @ cs rochester edu
April 10, 2008
