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15-859B Machine Learning Theory, Spring 2006

Course Information

**Lectures:** Mon/Wed 1:30-2:50, Wean 5409.
**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.