Date 
Time 
Place 
Topic 
Handouts 
Jan 19 
56pm

GHC 6115 
Probability Review

Slides 
Jan 26 
56pm

NSH 3305 
Naive Bayes

Slides 
Feb 3 
1:302:50pm

Margaret Morrison A14 
Review: logistic regression, Gaussian naive Bayes,
linear regression, and their connections.
New materials:
biasvariance decomposition, biasvariance tradeoff, overfitting,
regularization, and feature selection

Slides 
Feb 9 
56pm

NSH 3305 
Bayes Nets: Representation

Slides 
Feb 16 
56pm

GHC 6115 
Bayes Nets: Inference & DSeparation

Slides 
Feb 23 
56pm 
GHC 6115 
EM Algorithm and Midterm Exam Review 
EM Slides
Midterm
Exam Review (Part 1) 
Mar 2 
56pm 
NSH 3305 
Midterm Exam Review 
Midterm
Exam Review (Part 2) 
Mar 16 
56pm 
NSH 3305 
VC Dimensionality & Midterm Recap 
Slides 
Mar 23 
56pm 
NSH 3305 
Recap: training, testing, true errors and
overfitting. PAC learning with finite hypothesis space PAC
learning with infinite hypothesis space (VC bounds) Mistake bounds
Semisupervised learning 
Slides 
Mar 30 
56pm 
NSH 3305 
HMM (ForwardBackward, Veterbi, EM for Learning),
Neural Network 
Slides 
Apr 6 
56pm 
NSH 3305 
Principal Components Analysis, Independent
Component Analysis, Canonical Correlation Analysis, Fisher's Linear
Discriminant, Topic Models and Latent Dirichlet Allocation. 
Slides 
Apr 13 
56pm 
NSH 3305 
Support Vector Machines, Kernel Methods 
Slides 
Apr 20 
56pm 
NSH 3305 
Active Learning 
Slides 
Apr 27 
56pm 
NSH 3305 
Reinforcement Learning 
Slides 
May 4 
56pm 
NSH 3305 
Final Review 
Slides 