Description: This course covers statistical pattern recognition and learning theory. Topics include Supervised Learning, Linear Models, Statistical Decision Theory Linear Methods for Regression Linear Methods for Classification Kernel Methods Model selection and assesment (other topics as time permits)
W 4:30 pm - 7:10 pm, Innovation Hall 136
Textbooks:
Pattern Recognition and Machine Learning by Bishop
A first course in Machine Learning by Rogers and Girolami 2nd Edition
Grading:
Exams: (Midterm 10/16, in class; final 12/11) 60%
Assignments: 40%
Office hours: By appointment