Computer Science and EngineeringCSE 242 Machine Learning

Introduction to machine learning algorithms. Covers learning models from fields of statistical decision theory and pattern recognition, artificial intelligence, and theoretical computer science. Topics include classification learning and the Probably Approximately Correct (PAC) learning framework, density estimation and Bayesian learning, EM, regression, and online learning. Provides an introduction to standard learning methods such as neural networks, decision trees, boosting, nearest neighbor, and support vector machines. Requirements include one major experimental learning project or theoretical paper. Students may not receive credit for both this course and course 142. (Formerly CMPS 242.)

Requirements

Enrollment is restricted to graduate students in the computer engineering and computer science master's programs; and students in the following doctoral programs: computer engineering, computer science, applied mathematics and statistics, biomolecular engineering and bioinformatics, electrical engineering, and technology information management. Others may enroll by permission of the instructor.

Credits

5

Quarter offered

Fall, Spring

Instructor

N. Norouzi, M Warmuth, D. Helmbold