15-681 MACHINE LEARNING, Fall 1995

see the Fall 1998 version for more up-to-date material

School of Computer Science, Carnegie Mellon University

Instructor: Tom Mitchell

Teaching Assistant: Matt Glickman

When: Tues,Thurs 12:00-1:20, Wean Hall 5205

Online slides and other handouts



Machine Learning is concerned with computer programs that automatically improve their performance through experience. Machine Learning methods have been applied to problems such as learning to drive an autonomous vehicle, learning to recognize human speech, learning to detect credit card fraud, and learning strategies for game playing. This course covers the primary approaches to machine learning from a variety of fields, including inductive inference of decision trees, neural network learning, statistical learning methods, genetic algorithms, bayesian methods, explanation-based learning, and reinforcement learning. The course will also cover theoretical concepts such as inductive bias, the PAC learning framework, reductions among learning problems, and Occam's Razor. Programming assignments include experimenting with various learning problems and algorithms. This course is a combination upper-level undergraduate and introductory graduate course. CS Ph.D. students can obtain one core credit unit by arrangement with the instructor.

Note to people outside CMU

Feel free to use the slides and materials available online here. Please email Tom.Mitchell@cmu.edu with any corrections or improvements.

See also the Fall 1994 version of the course, including midterm and final.