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Course Offerings

Course Details

Spring 2011-2012
* PHI 218 / ELE 218 / EGR 218 (EC)  

Learning Theory and Epistemology

Gilbert H. Harman
Sanjeev R. Kulkarni

A broad and accessible introduction to contemporary statistical learning theory as a response to the philosophical problem of induction. It is intended for students of all backgrounds. Topics covered include pattern recognition, the Bayes rule, nearest neighbor methods, neural networks, and support vector machines.

Sample reading list:
Harman & Kulkarni, An Elementary Introduction to Statistical Learning Theory

Reading/Writing assignments:
Typical Weekly Assignments: One or two hours of reading, four hours of homework problems.

Requirements/Grading:
Final Exam - 35%
Term Paper(s) - 20%
Class/Precept Participation - 10%
Problem set(s) - 35%

Prerequisites and Restrictions:
No specific course prerequisites, but the course will require analytical and logical thinking..

Other information:
Due to the nature of the course, for each topic covered on both homework and exams, there will be questions that are more problem-solving in nature and questions that are more discursive in nature.

Schedule/Classroom assignment:

Class numberSectionTimeDaysRoomEnrollmentStatus
42510 L01 11:00 am - 11:50 am T Th   McCosh Hall   62   Enrolled:44 Limit:60
P01 5:30 pm - 6:20 pm Th   Friend Center of Engineering   202   Enrolled:0 Limit:13
P02 1:30 pm - 2:20 pm F   Friend Center of Engineering   202   Enrolled:0 Limit:13
44206 P03 TBA        Enrolled:0 Limit:0 Canceled
44207 P04 TBA        Enrolled:0 Limit:0 Canceled