| Week |
Topic |
Reading |
Assignment |
| Jan 8 |
Administrative Basic concepts, prerequisites Decision trees Regression Trees |
Background:
Other Decision Tree notes |
HW1
due Jan 29 |
| Jan 15 |
Linear regression Logistic regression Gradient Descent |
DHS ch 5 KMPP ch 7,8 |
|
| Jan 22 |
Perceptrons Neural Networks |
Other slides (Mitchell) Mitchell chapter DHS ch6 |
|
| Jan 29 |
Probabilities
Primer, Bayesian statistics Generative Methods Fitting parameters to data, Maximum Likelihood |
DHS
ch 2,3 KMPP ch 2,3 Andrew Ng Lecture Notes |
HW2 due Feb 19 |
| Feb 5 | Gaussian
Discriminant Analysis Naive Bayes Smoothing |
DHS
ch 2,3 KMPP ch 3,4 |
|
| Feb
12 |
Graphical Models, factor graphs EM algorithm, Gaussian mixtures |
Graphical
models - Bishop slides KMPP ch 20 , KMPP ch 11(EM) EM code, example EM wikipedia notes , EM Berkeley notes , EM other notes EM slides set1 , set2 |
|
| Feb
19 |
Boosting, AdaBoost Online Learning, Hedge Theory |
DHS ch 9 KMPP ch 16 boosting testing error proof AdaBoost/Hedge paper boosting preferences/ranking |
HW3 due Mar 12 |
| Feb
26 |
Active Learning VC dimension Multiclass: ECOC |
VC dimension slides DHS ch 9.5 KMPP ch 16.6 |
|
| Mar
5 |
SPRING
BREAK |
||
| Mar
12 |
Support
Vector Machines Convex Optimization , slides |
DHS ch 5.11 KMPP ch 14 saddle-point Chris Burges SVM tutorial , SVM on wikipedia SVM and VC dimension |
Project Proposal due HW4 due Apr 2 |
| Mar
19 |
Support Vector Machines SMO, Kernels (notes scan, some from Andrew Ng) |
Andrew NG SVM notes kernel slides |
|
| Mar 26 |
Locality Parzen Windows K-Nearest Neighbor Learning with harmonic functions |
KMPP ch14, ch1 DHS ch 4 |
|
| Apr
2 |
Collaborative Filtering Learning to Rank: RankBoost, LambdaMart |
Project Milestone |
|
| Apr
9 |
Clustering, KMeans Principal Component Analysis Feature Selection |
||
| Apr
16 |
Project Presentation |
Project Presentation | |
| Apr
23 |
Project due |