Instructor:
Dan Gildea office hours Tu/Th 23pm
TA: Iftekhar Naim office hours M/W 2:303:25pm
Location: M/W 3:254:40pm, Dewey 2110E
Homeworks
Lecture Notes
 Vector calculus review: 3:25pm Fri 1/18, CSB 632
 Python help session: 5pm Tue 1/22, CSB 601
Required text: Kevin P. Murphy, Machine Learning: A Probabilistic Perspective
The following are useful references in addition
to the reading material assigned for each class:
 Christopher M. Bishop, Pattern Recognition and Machine Learning.
 Stuart Russell and Peter Norvig, Artificial Intelligence, A Modern Approach.
 Larry Wasserman, All of Statistics, 2004.
Readings not taken from Murphy 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 
1/16 
Probability Theory 
independence, bayes rule
 murphy ch 1 
1/23 
Information Theory 
entropy, kldistance, coding
 murphy ch 2 
1/28 
Probabilistic Inference 
priors on continuous variables
 murphy 3.2 
1/30 
Probabilistic Inference 
priors: bayesian reasoning, MAP
 murphy 3.4 
2/4 
Perceptron 
stochastic gradient descent
 murphy 8.5 
2/6 
Backpropagation 
DP for gradient descent
 murphy 16.5 
2/11 
Support Vectors 
strong duality
 murphy 14.5 
2/13 
Support Vectors 
the kernel trick
 murphy 14.4 
2/18 
Probabilistic Inference 
message passing
 murphy 20.2 
2/20 
Tree decomposition 
cyclic graphs
 murphy 20.4 
2/25 
Expectation Maximization 
L = Q + H + D
 murphy 11.4 
2/27 
Expectation Maximization 
mixture of gaussians
 murphy 11.4 
3/4 
Sampling 
Markov Chain Monte Carlo
 murphy 24.3 
3/6 
Sampling 
Gibbs Sampling
 murphy 24.2 
3/18 
Learning Theory 
Error bounds
 kearns and vazirani ch 1, 3 
3/20 
Review 
 
3/25 
Midterm 
 
3/27 
Midterm Solutions 


4/1 
Eigenvectors 
least squares, PCA
 murphy 12.2 
4/3 
Logistic Regression 
maximum entropy
 murphy 8.3 
4/8 
Optimization 
LBFGS
 nocedal 8.1, 9.1 
4/10 
Hidden Markov Models 
forwardbackward
 murphy 17.3, 17.4 
4/15 
Particle Filters 
slam
 murphy 23.5 
4/17 
Reinforcement Learning 
qlearning
 sutton ch 3, 4.3, 4.4, 6.1, 6.5, 7.2, 11.1 
4/22 
Sparse Linear Models 
lasso
 murphy 13.3, 13.4 
4/24 
Sparse Linear Models 
sparse coding
 murphy 13.8, lee 
4/29 
Uniform Deviation Inequalities 
Daniel Stefankovic
 devroye ch3 
5/1 
Review 
come to class with questions!
 
Final Exam: Monday May 6, 8:3011:30am, Dewey 2110E.
Grading
 Final exam: 35%
 Homeworks: 30%
 Midterm: 25%
 NB: 5%
 Class participation: 5%
gildea @ cs rochester edu
May 3, 2013
