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Course Details

Fall 2018-2019
ELE 535   No 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 machine learning and statistical pattern classification are discussed. Topics include Bayesian classification, regression, regularization, maximum margin classification, kernels, neural networks and 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.

Requirements/Grading:
Mid Term Exam - 25%
Final Exam - 50%
Problem set(s) - 25%

Other Requirements:
Not Open to First Year Undergraduates.

Schedule/Classroom assignment:

Class numberSectionTimeDaysRoomEnrollmentStatus
22606 L01 10:00:00 am - 10:50:00 am M W F   Friend Center   101   Enrolled:66 Limit:50 Closed