Instructor: Bo Waggoner
Time: Tue/Thu 8:00am - 9:15am
Meets synchronously, remotely (Zoom link is on Canvas page)
Presents the underlying theory behind machine learning in proofs-based format. Answers fundamental questions about what learning means and what can be learned via formal models of statistical learning theory. Analyzes some important classes of machine learning methods. Specific topics may include the PAC framework, VC-dimension and Rademacher complexity. Recommended prerequisites: APPM 4440 and CSCI 5622.
The course will be similar to Prof. Stephen Becker's version.