ELEG 602 - Advanced Machine Learning

Credit(s): 3

ADVANCED MACHINE LEARNING
Component: Lecture
This advanced course on machine learning features an in-depth treatment of modern learning theory and emphasizes its interplay with real-world learning algorithms. The main goal of this course is to get students started in research, in particular, to help them transition from knowing how to implement towards exploring why to do this and how to do better. Students will carry out research projects, and the hope is that some of these projects will result in research papers that can be published in top machine learning venues.

Major topics covered include the following. PAC Learning Framework: Empirical Risk Minimization.
Repeatable for Credit: N Allowed Units: 3 Multiple Term Enrollment: N Grading Basis: Student Option
RESTRICTIONS: Exposure to a first course on machine learning (e.g. ELEG/FSAN 815), or knowledge on probability theory (e.g. ELEG 310) and linear algebra (MATH 351); mathematical maturity in general.

Course Typically Offered: Spring