M 1/27

Machine Learning Foundations

Overview, applications, settings

Bishop 1

W 1/29

Math Review

Calculus, linear algebra, optimization

Linear algebra

Bishop Appendix C

M 2/3


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

W 2/5

Decision Trees

Construction, pruining, over-fitting

Nilsson chapter 6

Supervised Learning: Linear Methods

M 2/10


Least squares and regression

Bishop 3

W 2/12


Logistic Regression

Bishop 4

M 2/17

Generative vs. discriminative

Naive Bayes and Logistic Regression

Ng and Jordan, 2001

W 2/19

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 2/24

Support Vector Machines

Max-margin classification and optimization

Bishop 7.1

Chris Burges SVM Tutorial

Bishop Appendix E

W 2/26

Kernel Methods

Dual optmization, kernel trick

Bishop 6.1, 6.2

M 3/3

Instance based learning


Bishop 2.5

Mitchell 8-8.4

W 3/5

Neural Networks

Neural Network models

Bishop 5.1,5.2,5.3,5.5

M 3/10

Ensemble Methods


Bishop 14.1,14.2,14.3

A Short Introduction to Boosting

Unsupervised Learning

W 3/12


Expectation-Maximization and k-means

Bishop 9

M 3/24

EM and Clustering 1

Gaussian mixture models

Bishop 9

W 3/26

EM and Clustering 2

The EM Algorithm

Bishop 9

Graphical Models

M 3/31

Graphical models 1

Bayesian networks and conditional independence

Bishop 8.1, 8.2

W 4/2

Graphical models 2

MRFs and Exact inference

Bishop 8.3, 8.4

M 4/7

Graphical models 3


Bishop 8.3, 8.4

W 4/9

Sequential graphical models 1

Max Sum and Max Product

Bishop 13.1,13.2

M 4/14

Sequential graphical models 2

HMMs and CRFs

Sutton, McCallum CRF tutorial

Other Topics

W 4/16

Current Trends in Supervised Learning

M 4/21


W 4/23

Dimensionality reduction

PCA, probabilistic PCA, LDA

Bishop 12.1,12.2,12.3

Max Welling's PCA Tutorial

Jonathon Shlens's PCA Tutorial

M 4/28

Practical Machine Learning

W 4/30

Semi-supervised learning

Guest Lecture

Jerry Zhu Semi-Supervised Learning Tutorial

W 5/14


Final Poster Session Time

Project presentations