| Date |
Time |
Place |
Topic |
Handouts |
| Jan 19 |
5-6pm
|
GHC 6115 |
Probability Review
|
Slides |
| Jan 26 |
5-6pm
|
NSH 3305 |
Naive Bayes
|
Slides |
| Feb 3 |
1:30-2:50pm
|
Margaret Morrison A14 |
Review: logistic regression, Gaussian naive Bayes,
linear regression, and their connections.
New materials:
bias-variance decomposition, bias-variance tradeoff, overfitting,
regularization, and feature selection
|
Slides |
| Feb 9 |
5-6pm
|
NSH 3305 |
Bayes Nets: Representation
|
Slides |
| Feb 16 |
5-6pm
|
GHC 6115 |
Bayes Nets: Inference & D-Separation
|
Slides |
| Feb 23 |
5-6pm |
GHC 6115 |
EM Algorithm and Midterm Exam Review |
EM Slides
Midterm
Exam Review (Part 1) |
| Mar 2 |
5-6pm |
NSH 3305 |
Midterm Exam Review |
Midterm
Exam Review (Part 2) |
| Mar 16 |
5-6pm |
NSH 3305 |
VC Dimensionality & Midterm Recap |
Slides |
| Mar 23 |
5-6pm |
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
Semi-supervised learning |
Slides |
| Mar 30 |
5-6pm |
NSH 3305 |
HMM (Forward-Backward, Veterbi, EM for Learning),
Neural Network |
Slides |
| Apr 6 |
5-6pm |
NSH 3305 |
Principal Components Analysis, Independent
Component Analysis, Canonical Correlation Analysis, Fisher's Linear
Discriminant, Topic Models and Latent Dirichlet Allocation. |
Slides |
| Apr 13 |
5-6pm |
NSH 3305 |
Support Vector Machines, Kernel Methods |
Slides |
| Apr 20 |
5-6pm |
NSH 3305 |
Active Learning |
Slides |
| Apr 27 |
5-6pm |
NSH 3305 |
Reinforcement Learning |
Slides |
| May 4 |
5-6pm |
NSH 3305 |
Final Review |
Slides |
