Date

Topics

Readings

Introduction

W 9/5

Machine Learning Foundations

Overview, applications, settings

Bishop 1

M 9/10

Math Review

Calculus, linear algebra, optimization

Linear algebra

Bishop Appendix C

W 9/12

Probability

Probability, stats

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

Probability:

Bishop Chapter 2

Bishop Appendix B

Andrew Moore Tutorial

Tom Minka's nuances of probability (advanced)

Wolfram Probability and stats

M 9/17

Decision Trees

Construction, pruining, over-fitting

Nilsson chapter 6

Supervised Learning: Linear Methods

W 9/19

Regression

Least squares and regression

Bishop 3

M 9/24

Classification

Logistic Regression

Bishop 4

W 9/26

Generative vs. discriminative

Naive Bayes and Logistic Regression

Ng and Jordan, 2001

M 10/1

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

W 10/3

Support Vector Machines

Max-margin classification and optimization

Bishop 7.1

Chris Burges SVM Tutorial

Bishop Appendix E

M 10/8

Kernel Methods

Dual optmization, kernel trick

Bishop 6.1, 6.2

W 10/10

Instance based learning

Nearest-neighbors

Bishop 2.5

Mitchell 8-8.4

Tue 10/16

Neural Networks

Neural Network models

Bishop 5.1,5.2,5.3,5.5

W 10/17

Ensemble Methods

Boosting

Bishop 14.1,14.2,14.3

A Short Introduction to Boosting

Unsupervised Learning

M 10/22

Clustering

Expectation-Maximization and k-means

Bishop 9

W 10/24

EM and Clustering 1

Gaussian mixture models

Bishop 9

M 10/29

EM and Clustering 2

The EM Algorithm

Bishop 9

Graphical Models

W 10/31

Graphical models 1

Bayesian networks and conditional independence

Bishop 8.1, 8.2

M 11/5

Graphical models 2

MRFs and Exact inference

Bishop 8.3, 8.4

W 11/7

Graphical models 3

Inference

Bishop 8.3, 8.4

M 11/12

Sequential graphical models 1

Max Sum and Max Product

Bishop 13.1,13.2

W 11/14

Sequential graphical models 2

HMMs and CRFs

Sutton, McCallum CRF tutorial

Other Topics

M 11/19

Current Trends in Supervised Learning

W 11/21

Thanksgiving Break

No class


M 11/26

Dimensionality reduction

PCA, probabilistic PCA, LDA

Bishop 12.1,12.2,12.3

Max Welling's PCA Tutorial

Jonathon Shlens's PCA Tutorial

W 11/28

Practical Machine Learning

M 12/3

Semi-supervised learning

Guest Lecture

Jerry Zhu Semi-Supervised Learning Tutorial

W 12/5

TBA

TBA

TBA

(Day and time subject to change)

Final Poster Session Time

Project presentations