Date

Topics

Readings

Introduction

Th 8/28

Machine Learning Foundations

Overview, applications, settings

Bishop 1

Tu 9/2

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

Th 9/4

Decision Trees

Construction, pruining, over-fitting

Nilsson chapter 6

Supervised Learning: Linear Methods

Tu 9/9

Regression

Least squares and regression

Bishop 3

Th 9/11

Classification

Logistic Regression

Bishop 4

Tu 9/16

Naive Bayes

Ng and Jordan, 2001

Th 9/18

Online Learning for Structured Prediction

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

Tu 9/23

Support Vector Machines

Max-margin classification and optimization

Bishop 7.1

Chris Burges SVM Tutorial

Bishop Appendix E

Th 9/25

Kernel Methods

Dual optmization, kernel trick

Bishop 6.1, 6.2

Tu 9/30

Instance based learning

Nearest-neighbors

Bishop 2.5

Mitchell 8-8.4

Th 10/2

Ensemble Methods

Boosting

Bishop 14.1,14.2,14.3

A Short Introduction to Boosting

Tu 10/7

Deep Networks 1

Neural Network models

Bishop 5.1,5.2,5.3,5.5

Th 10/9

Deep Networks 2

Deep learning

One of the Following:

Deep Learning for NLP (without Magic)

ICML Deep Learning Tutorial (LeCun and Ranzato)

Geoffrey Hinton Tutorial (Video)

Tutorial on Deep Learning and Applications (Lee)

Unsupervised Learning

Tu 10/14

Midterm Exam

Th 10/16

No Class

Monday schedule

Tu 10/21

Clustering

Expectation-Maximization and k-means

Bishop 9

Th 10/23

EM and Clustering 1

Gaussian mixture models

Bishop 9

Tu 10/28

EM and Clustering 2

The EM Algorithm

Bishop 9

Graphical Models

Th 10/30

Graphical models 1

Bayesian networks and conditional independence

Bishop 8.1, 8.2

Tu 11/4

Graphical models 2

MRFs and Exact inference

Bishop 8.3, 8.4

Th 11/6

Graphical models 3

Inference

Bishop 8.3, 8.4

Tu 11/11

Sequential graphical models 1

Max Sum and Max Product

Bishop 13.1,13.2

Th 11/13

Sequential graphical models 2

HMMs and CRFs

Sutton, McCallum CRF tutorial

Other Topics

Tu 11/18

Dimensionality reduction

PCA, probabilistic PCA, LDA

Bishop 12.1,12.2,12.3

Max Welling's PCA Tutorial

Jonathon Shlens's PCA Tutorial (more mathematically oriented)

Lindsay Smith Tutorial (good at the basics)

Th 11/20

Current Trends in Supervised Learning

Tu 12/2

TBA

Th 12/4

Practical Machine Learning