The course web page is at http://zoo.cs.yale.edu/classes/cs463. Please check it often.
Machine Learning by Tom M. Mitchell. McGraw-Hill, 1997. (Artificial Intelligence perspective.)
An Introduction to Computational Learning Theory by Michael J. Kearns and Umesh V. Vazirani. MIT Press, 1994. (Theoretical Computer Science perspective.)
Introduction to Machine Learning by Ethem Alpaydin. MIT Press, 2004. (Statistical perspective.)
The prerequisites are Computer Science 202 (Mathematical Tools for Computer Science) and Computer Science 223 (Data Structures and Programming Techniques). Computer Science 365 (Design and Analysis of Algorithms) is recommended, but not required. Knowledge of linear algebra and probability and statistics will also be helpful. Please talk to the instructor if you have questions about your preparation. The course requirements consist of class attendance and discussion, assigned readings, problem sets, class presentations, quizzes and a midterm, and a final project.
Paradigms and algorithms for learning classification rules and more complex behaviors from examples and other kinds of data. Topics may include version spaces, decision trees, artificial neural networks, Bayesian networks, instance based learning, genetic algorithms, reinforcement learning, inductive logic programming, the MDL principle, the PAC model, VC dimension, sample bounds, boosting, support vector machines, queries, grammatical inference, and inductive inference.
For lecture summaries from the last time the course was taught, please see [Lecture Log from 2005].