* PHI 218 / ELE 218 / EGR 218 (EC) Graded A-F, P/D/F, Audit
Learning Theory and Epistemology
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
Typical Weekly Assignments: One or two hours of reading, four hours of homework problems.
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..
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.
|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 202||Enrolled:0 Limit:13|
|P02||1:30 pm - 2:20 pm||F||Friend Center 202||Enrolled:0 Limit:13|