Course Overview

Theoretical and practical issues in modern machine learning techniques. Topics include statistical foundations, supervised and unsupervised learning, decision trees, hidden Markov models, neural networks, and reinforcement learning. Minimal overlap with Computer Science 270.

Info

Instructor
Ron Parr
Office: D209 LSRC
Email: parr at cs dot duke dot edu

TA team
Mac Mason, Christopher Painter-Wakefield, and Jason Pazis
Email: mac at cs dot duke dot edu, paint007 at cs dot duke dot edu, jpazis at cs dot duke dot edu

Office hours
Location: D301 LSRC (loft to the right of the top of the D-wing stairwell)
Times: Tuesdays 10-11, Wednesdays 1-2