Instructor:
Dan Gildea office hours Tu/Th 12pm Wegmans Hall 3019
TAs:
Location: M/W 910:15am, Wegmans 1400
The course is full. If spaces become available, I will
add people who hand in the first homework. If you would like to
try to add, write "NEED TO ADD" on the top of your assignment, along
with your name, program (eg, BS in CS) and year.
Prereqs: Probability, Linear Algebra, Vector Calculus.
Homeworks
Lecture notes
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.
 Larry Wasserman, All of Statistics, 2004.
Syllabus
On  we will cover  which means that after class you will understand  if before class you have read 
1/16 
Regression and Classification 
constrained optimization, perceptron
 bishop 1.2, 1.4, app E 
1/23 
Logistic Regression 
stochastic gradient descent
 bishop 4.3 
1/28 
Backpropagation 
DP for gradient descent
 bishop 5.1, 5.2, 5.3 
1/30 
Deep Learning 
dropout
 Krizhevsky 2012 
2/4 
Support vectors 
maxmargin
 bishop 3.1, 4.1 
2/6 
Support Vectors 
strong duality
 bishop 7.1 
2/11 
Support Vectors 
the kernel trick
 bishop 6.1, 6.2 
2/13 
Hidden Markov Models 
forwardbackward
 bishop 13.2 
2/18 
Graphical Models 
bayes ball
 
2/20 
Probabilistic Inference 
message passing
 bishop 8.4 
2/25 
Tree decomposition 
cyclic graphs
 koller and friedman 
2/27 
Tree decomposition cont'd 
vertex elimination
 
3/4 
Review 
 
3/6 
Midterm 
 
3/18 
Midterm Solutions 
 
3/20 
Expectation Maximization 
L = Q + H + D
 bishop 9 
3/25 
Expectation Maximization 
mixture of gaussians
 bishop 9 
3/27 
EM for HMM 
minimum bayes risk
 
4/1 
Sampling 
Markov Chain Monte Carlo
 bishop 11.2 
4/3 
Metropolis Hastings 
detailed balance
 bishop 11.2 
4/8 
Gibbs sampline 
annealing
 bishop 11.3 
4/10 
Learning Theory 
PAC
 Kearns and Vazirani 
4/15 
Learning Theory 
VC dimension
 
4/17 
Optimization 
SGD convergence
 Ruder 2016 
4/22 
PCA 
Eigenvectors
 
4/25 
Reinforcement Learning 
qlearning
 sutton ch 3, 4.3, 4.4, 6.1, 6.5, 7.2, 11.1 
4/29 
Something fun 
 
5/1 
Review 
come to class with questions!
 
Final Exam: Wed May 8, 8:3011:30am, Douglass Ballroom.
Grading
 Homeworks: 50%
 Final exam: 30%
 Midterm: 20%
gildea @ cs rochester edu
January 23, 2019
