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Course Details

Fall 2018-2019
* ELE 435   No Audit

Machine Learning and Pattern Recognition

Peter J. Ramadge

The course is an introduction to the theoretical foundations of machine learning and patter 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:
Ramadge, P. J., Machine Learning and Patter Recognition (course notes)
Bishop, Christopher M, Pattern Recognition and Machine Learning

Reading/Writing assignments:
Course notes, plus selected articles are handed out on Blackboard. Additional topics covered in the readings include: Multivariate probability. Projection pursuit. Some advanced material on convex sets and functions.

Mid Term Exam - 25%
Final Exam - 50%
Programming Assignments - 13%
Problem set(s) - 12%

Other Requirements:
Not Open to First Year Undergraduates.

Prerequisites and Restrictions:
Prerequisites include linear algebra (MAT 202 or MAT 204 or equivalent), a course on basic probability (e.g. ORF 309 or equivalent) and basic programming skills (e.g., prior experience or COS 126). Some prior exposure to multivariable differential calculus and basic machine learning is helpful, but not required (e.g. COS 324, ORF 350)..

Schedule/Classroom assignment:

Class numberSectionTimeDaysRoomEnrollmentStatus
23374 L01 10:00:00 am - 10:50:00 am M W F   Friend Center   101   Enrolled:69 Limit:100