Skip over navigation

Course Offerings

Course Evaluation Results

Course Details

Fall 2016-2017
ELE 535   Graded A-F, P/D/F, Audit

Machine Learning and Pattern Recognition

Peter J. Ramadge

The course is an introduction to the theoretical foundations of machine learning and pattern recognition. A variety of classical and recent results in statistical pattern classification and non-parametric estimation will be discussed. Topics include Bayesian pattern classification; parametric methods; nearest neighbor classification; density estimation; PAC learning; neural networks; stochastic approximation.

Sample reading list:
Christopher M. Bishop, Pattern Recognition and Machine Learning
Recent Journal Papers,

Reading/Writing assignments:
Problem set and a lab report.

Mid Term Exam - 30%
Final Exam - 40%
Papers - 10%
Problem set(s) - 20%

Other Requirements:
Not Open to First Year Undergraduates.

Prerequisites and Restrictions:
ELE 525 or Permission Of Instructor..

Schedule/Classroom assignment:

Class numberSectionTimeDaysRoomEnrollmentStatus
22865 L01 10:00:00 am - 10:50:00 am M W F   Engineering Quad B-Wing   B205   Enrolled:47 Limit:60