Instructor:  Milos Hauskrecht
5329 Sennott Square, x4-8845
e-mail: milos@cs.pitt.edu
office hours: T 2:00-3:30pm, W 4:00-5:00pm

Lecture notes

• Lecture 1 (Modeling uncertainty, BBNs).
• Lecture 2 (Inference and Learning).

Homework

Text of the homework assignment

Submission instruction:

• Homework is due on September 19, 2002 at noon
• Please submit it to Malinda Weems, secretary to the chair of the department

CS 2750 Machine Learning

• Offered in Spring 2003
• Course webpage from last year.

Abstract
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.