Time: Monday, Wednesday 4:00-5:20pm,
MIB 113
Instructor: Milos
Hauskrecht
Computer Science Department
MIB 318
phone: x4-8845
e-mail: milos@cs.pitt.edu
office hours: by appointment
TA: Mathew Bell
Computer Science Department
MIB 317
phone: x4-8842
e-mail: mbell@cs.pitt.edu
office hours: Monday, Tuesday 10 - 11:30 am
Term projects reports are due on Wednesday, April 24, 2002 at 4pm.
Project presentations will be held on the same day, April 24, 2002, starting at 4pm (for about 3 1/2 hours). The presentations should be:
Course description
Lectures
Other course information
(readings, grading, homeworks)
Term projects (Project ideas and other resources )
Matlab
The goal of the field of machine learning is to build computer systems that learn from experience and that are capable of adapting to their environments. Learning techniques and methods developed by researchers in this field have been successfully applied to a variety of learning tasks in a broad range of areas, including, for example, text classification, gene discovery, financial forecasting, credit card fraud detection, collaborative filtering, design of adaptive web agents and others.
This introductory machine learning course will give an overview of many techniques and algorithms in machine learning, beginning with topics such as simple concept learning and ending up with more recent topics such as boosting, support vector machines, and reinforcement learning. The objective of the course is not only to present the modern machine learning methods but also to give the basic intutions behind the methods as well as, a more formal understanding of how and why they work.
Topics (subject to minor changes)
Undergraduate level knowledge of matrices and linear algebra (CS 0280), probability (CS 1151), programming (CS 1501) or equivalent, or the permission of the instructor.
Lectures | Topic(s) | Assignments | |
---|---|---|---|
January 7 |
Administrivia and course overview.
Readings:
|
. | |
January 9 |
Designing a learning system.
Readings:
|
. | |
January 14 |
Designing a learning system (cont.)
Readings.
|
. | |
January 16 | Matlab tutorial | . | |
January 23 |
Regression. Linear regression
Readings:
|
Assignment 1 out (due January 30) Data and programs for assignment 1 |
January 28 |
Linear regression (cont.). Logistic regression.
Readings.
|
. | January 30 |
Classification. Logistic regression.
Readings
|
Assignment 2 out (due February 6) Programs and data for assignment 2 |
February 4 |
Classification. Generative model.
Readings.
|
. | February 6 |
Multilayer neural networks
Readings.
|
Assignment 3 out (due February 13) Programs and data for assignment 3 |
February 11 |
Multi-way classification
Readings.
|
. | February 13 |
Support vector machines
Readings.
|
. | February 18 |
Density estimation
Readings.
|
Solutions to PS-1 | February 20 |
Bayesian belief networks
Readings.
|
Problem set 4 Programs and data for PS 4 |
February 25 |
Bayesian belief networks
Readings. The same as last lecture |
Solutions to PS-2 | February 27 |
Learning Bayesian belief networks.
Readings.
|
. | . | Spring break (March 2-March 9) | . | March 11 |
Learning with hidden variables and missing
values. Expectation-maximization.
Readings.
|
Solutions to PS-3
Homework 5 out (due March 25) |
March 13 | Midterm exam | . | March 18 | Project proposals | . | March 20 |
Mixture of Gaussians. Clustering.
Readings.
|
. | March 25 |
Dimensionality reduction. Feature selection.
Readings.
|
Solutions to PS4 | March 27 |
Decision trees. Ensamble methods. Mixture of experts. Hierachical mixtures of experts. Readings.
|
. | April 1 |
Ensamble methods. Bagging. Boosting. Readings.
|
. | April 3 |
Non-parametric density estimation. Readings.
|
. | April 8 |
Bayesian decision theory. Reinforcement learning with immediate rewards. Readings.
|
. | April 10 |
Reinforcement learning
Readings.
|
. |
April 15 |
Hidden Markov models
Readings.
|
. | |
April 17 |
Concept learning.
Readings.
|
. | |
April 24 | Term projects: reports + presentations. |
Solutions to PS5 |
Readings will be distributed electronically through the course web page
Primary readings:
M. Jordan, C. Bishop. Introduction to graphical
models. Local Upitt access only!!
Some readings for future classes (in electronic form)
Recommended books:
Chris Bishop. Neural networks for Pattern Recognition. Oxford University
Press, 1995.
R.O. Duda, P.E. Hart, D.G. Stork. Pattern Classification.
Second edition. John Wiley and Sons, 2000.
T. Mitchell. Machine Learning. Mc Graw Hill, 1997.
Other related books you may find useful:
Han, Kamber. Data mining. Concepts and Techniques. Morgan Kauffman, 2001.
The course grade will be determined roughly as follows :
The homework assignments will have mostly a character of projects
and will require you to implement some of the learning algorithms
covered during the semester and apply them.
No collaboration on homeworks is allowed. To implement homework assignments
you may use a programming language and graphic tools of your choice. However,
students are highly enouraged to use Matlab.
Term project is due at the end of the semester and accounts for about 40% of your grade. You can choose your own problem topic. You will be asked to write a short proposal for the purpose of approval and feedback. The project must have a distinctive and non-trivial learning or adaptive component. In general, a project may consist of a replication of previously published results, design of new learning methods and their testing, or application of machine learning to a domain or a problem of your interest.
Matlab is a mathematical tool for numerical computation and manipulation, with excellent graphing capabilities. It provides a great deal of support and capabilities for things you will need to run Machine Learning experiments. Upitt has a number of Matlab licences running on both unix and windows platforms. Click here to find out how to access Matlab at Upitt.
Files used during the tutorial on 01/16/02.
Other Matlab resources on the web:
Online
MATLAB documentation
Online
Mathworks documentation including MATLAB toolboxes