News :
- Welcome to first day of class!
- Join piazza: here
Location and Time :
Location : Uris Library, 2B02
Online: The class will also be available via zoom (but wont be recorded) at same time at:
Join Zoom Meeting (only cornell ID will be allowed):
https://cornell.zoom.us/j/97660628796?pwd=Wlgwd1IwQXRxQTNJYkMycGtlQ3Vadz09
Meeting ID: 976 6062 8796
Passcode: 039664
Time : Tue-Thu 1:00 PM to 2:15 PM (EST)
Office Hours : To be decided soon. On zoom or via email request.
Description :
We will discuss both classical results and recent advances in both statistical (iid batch) and online learning theory. We will also touch upon results in computational learning theory. The course aims at providing students with tools and techniques to understand inherent mathematical complexities of learning problems, to analyze and prove performance guarantees for machine learning methods and to develop theoretically sound learning algorithms.
Pre-requisite :
Student requires a basic level of mathematical maturity and ease/familiarity with theorems and proofs style material. Familiarity with probability theory, basics of algorithms and an introductory course on Machine Learning (CS 4780 or equivalent) are required. M.Eng. and undergraduate students require permission of instructor.
Grading :
Assignments :
There will be a total of 4 assignments covering 40% of your grade.
Term project :
There will be a term project due by the end of the course. The project is worth 60% of your grade. The project could be your choice of research problem approved by me for which you will submit a report by end of term or,