M 8/30


Overview, applications, history

Bishop 1

W 9/1

Math Review

Calculus, linear algebra, optimization

Linear algebra

Bishop Appendix C

M 9/6

Labor Day

No Class

W 9/8


Probability, stats

You do NOT need to read all
of these. However, you should
be familiar with the material.


Bishop Chapter 2

Bishop Appendix B

Andrew Moore Tutorial

Tom Minka's nuances of probability (advanced)

Wolfram Probability and stats

M 9/13

Machine Learning Foundations

Learning definitions, settings

Bishop 1

W 9/15

Decision Trees

Construction, pruining, over-fitting

Nilsson chapter 6

Supervised Learning: Linear Methods

M 9/20


Least squares and regression

Bishop 3

W 9/22


Logistic Regression

Bishop 4

M 9/27

Generative vs. discriminative

Naive Bayes and Logistic Regression

Ng and Jordan, 2001

W 9/29

Online methods

Perceptron, multi-class, structured

Blum. On-Line Algorithms in Machine Learning. 1996

Discriminative Training Methods for Hidden Markov Models:
Theory and Experiments with Perceptron Algorithms. EMNLP, 2002.

Bishop 4.1.2

Reducing Multiclass to Binary (Sections 1, 2, 3)

Supervised Learning: Non-Linear Methods

M 10/4

Support Vector Machines

Max-margin classification and optimization

Bishop 7.1

Chris Burges SVM Tutorial

Bishop Appendix E

W 10/6

Kernel Methods

Dual optmization, kernel trick

Bishop 6.1, 6.2

Tue 10/12

Instance based learning


Bishop 2.5

Mitchell 8-8.4

W 10/13

Neural Networks

Neural Network models

Bishop 5.1,5.2,5.3,5.5

M 10/18

Ensemble Methods


Bishop 14.1,14.2,14.3

A Short Introduction to Boosting

W 10/20

Current Trends in Supervised Learning

Unsupervised Learning

M 10/25


Expectation-Maximization and k-means

Bishop 9

W 10/27

EM and Clustering 1

Gaussian mixture models

Bishop 9

M 11/1

EM and Clustering 2

The EM Algorithm

Bishop 9

Graphical Models

W 11/3

Graphical models 1

Bayesian networks and conditional independence

Bishop 8.1, 8.2

M 11/8

Graphical models 2

MRFs and Exact inference

Bishop 8.3, 8.4

W 11/10

Graphical models 3


Bishop 8.3, 8.4

M 11/15

Sequential graphical models 1

Max Sum and Max Product

Bishop 13.1,13.2

W 11/17

Sequential graphical models 2

HMMs and CRFs

Sutton, McCallum CRF tutorial

Other Topics

M 11/22

Dimensionality reduction

PCA, probabilistic PCA, LDA

Bishop 12.1,12.2,12.3

Max Welling's PCA Tutorial

W 11/24

Thanksgiving Break

No class

M 11/29

Guest Lecture: Current Trends in Unsupervised Learning

Large-scale nonparametric methods

W 12/1

Semi-supervised learning

Guest Lecture

Jerry Zhu Semi-Supervised Learning Tutorial

M 12/13 9am

Final Poster Session Time

Project presentations