This course covers several topics in machine learning theory and optimization.
Topics include (according to time and interest):
Gaussian mean estimation
Asymptotics
Concentration inequalities
Uniform convergence
Online learning
Kernel methods
Sampling and optimization
Class meets on Thursday 2-4 pm @AB 107 (50 St. George St.)
Final project reports should be submitted by April 15.
Homework 3 is out. Due Apr 4 in class.
Due date for HW2 is extended to Mar 25 1pm.
Homework 2 is out. Due Mar 21 in class.
No class in the reading week.
Homework 1 is out. Due Feb 7 in class.
Homework 0 is posted.