Instructor:  Milos Hauskrecht
Computer Science Department
5329 Sennott Square
phone: x4-8845
e-mail: milos@cs.pitt.edu
office hours: by appointment

TA:  Ali Alanjawi
Computer Science Department
5404 Sennot Square
phone: x4-1185
e-mail: alanjawi@cs.pitt.edu
office hours: Tuesday, Thursday: 1pm - 4pm

Quiz:

• Monday, April 21, 2003 at 2pm
• ~ 30 minutes
• Closed book
• Main ideas of methods covered after the midterm: EM, Dimensionality reduction, Clustering, Non-parametric methods, Decision trees, Mixtures of experts, Bagging and Boosting, Reinforcement learning.

Final projects:

• Project reports are due at 2pm on Wednesday April 23, 2003.
  • Submit your report in both the paper and the electronic forms. The electronic version should be e-mailed to milos@cs.pitt.edu.
  • Example project reports submitted by students last year:
    • Ali Alanjawi. Linear Versus Non-Linear Learning in the Context of Othello.
    • Gregory Khapfhammer. Improving Observation-Based Testing with Database Incorporation Heuristics.
    • Andrew Post. Learning functional clusters of genes in yeast gene expression data.
• Project presentations
  • Friday, April 25, 2003 at 1:00pm in 5313 Sennott Square
  • 10 minutes (ppt) presentations + 2 min for questions

!!! CS 3750 Advanced Topics in Machine Learning (ISSP 3535) !!!

• Offered in Fall 2003
• MW 4:00pm-5:20pm, 5313 Sennott Square
• Focus on recent advances in ML including kernel methods, variational approximations and learning.

Additional readings for the course including topics still to be covered

Course description
Lectures
Homeworks
Term projects
Matlab

The goal of the field of machine learning is to build computer systems that learn from experience and that are capable to adapt 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 models and algorithms used in modern machine learning, including linear models, multi-layer neural networks, support vector machines, density estimation methods, Bayesian belief networks, mixture models, clustering, ensamble methods, and reinforcement learning. The course will give the student the basic ideas and intuition behind these methods, as well as, a more formal understanding of how and why they work. Students will have an opportunity to experiment with machine learning techniques and apply them a selected problem in the context of a term project.

Prerequisites

Knowledge of matrices and linear algebra (CS 0280), probability (CS 1151), statistics (CS 1000), programming (CS 1501) or equivalent, or the permission of the instructor.

• R.O. Duda, P.E. Hart, D.G. Stork. Pattern Classification. Second edition. John Wiley and Sons, 2000.

• Trevor Hastie, Robert Tibshirani, Jerome Friedman. The elements of statistical learning. Springer, 2001.

• C. Bishop. Neural networks for Pattern Recognition. Oxford University Press, 1995.
• J. Han, M. Kamber. Data mining. Concepts and Techniques. Morgan Kauffman, 2001.
• T. Mitchell. Machine Learning. Mc Graw Hill, 1997.

The homework assignments will have mostly a character of projects and will require you to implement some of the learning algorithms covered during lectures. Programming assignmets will be implemented in Matlab. See rules for the submission of programs.

The assignments (both written and programming parts) are due at the beginning of the class on the day specified on the assignment. In general, no extensions will be granted.

Collaborations: You may discuss material with your fellow students, but the report and programs should be written individually.
 

The term project is due at the end of the semester and accounts for a significant portion 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.

Matlab tutorial files from 01/15/03.

Other Matlab resources on the web:

Online MATLAB  documentation
Online Mathworks documentation including MATLAB toolboxes

Course webpage from Spring 2002