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15-859B Machine Learning Theory, Spring 2014

Course Information

**Lectures:** Mon/Wed 10:30-11:50, GHC 4102.
**Instructor:** Avrim Blum
(GHC 8111).

**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 we prove about machine learning algorithms?
Can we design algorithms for interesting learning tasks with strong
guarantees on accuracy and amounts of data needed? (Why) is Occam's
razor a good idea and what does that even mean? What can we say about
the inherent ease or difficulty of learning problems? Can we devise
models that are both amenable to theoretical analysis and make sense
empirically? Addressing these questions will bring in connections to
probability and statistics, online algorithms, game theory,
complexity theory, information theory, cryptography, and
empirical machine learning research.

**Prerequisites: **A
Theory/Algorithms background or a Machine Learning background.

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

**Text (recommended):** 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)

**Web page:** http://www.cs.cmu.edu/~avrim/ML14/.

See also the Spring
2009 version of this course.