Instructor: Avrim Blum (Wean 4130, x8-6452).

Office Hours: TBD.

Credits: 12 Units

Course Description: This course will focus on theoretical aspects of machine learning. We will examine questions such as: What kinds of guarantees can one prove about learning algorithms? What are good algorithms for achieving certain types of goals? Can we devise models that are both amenable to mathematical analysis and make sense empirically? What can we say about the inherent ease or difficulty of learning problems? Addressing these questions will combine statistics, complexity theory, information theory, cryptography, game theory, and empirical machine learning research.

Prerequisites: Either 15-681/781 Machine Learning, or 15-750 Algorithms, or a strong Theory/Algorithms background.

Evaluation and Responsibilities: Grading will be based on 6 homework assignments, a final exam (worth about 2 homeworks), class participation, and a small class project. Students from time to time will also be asked to help with the grading of assignments.

Text: Kearns and Vazirani, "An introduction to computational learning theory" plus papers and notes for topics not in the book. (Roughly half of the topics are in the book)

See also the Spring 2004 version of this course.