ES 645

Optimization for Machine Learning

 Course Description

Optimization forms one of the main backbones of machine learning. This course aims to familiarize students with the various optimization techniques. A high-level view of the topics is as follows:

    Examples of papers to be covered here (more to be added)


Instructor: Anirban Dasgupta, Office: AB 6/407c. Please email for appointment. 

Teaching Assistants : Shrutimoy Das

Class :  TBD

Grading Policy

We will strictly follow the honor policy of IITGN. Collaboration in homeworks is allowed unless stated explicitly. Collaborations among class participants is allowed in homeworks, but everyone needs to write down their own answers and code as well as generate their own plots. Anyone with you discuss ideas with when solving the homework needs to be mentioned clearly. You are not expected to use Google or any other source for finding answers to homework questions unless explicitly allowed. 

Textbooks and References:

Expected prerequisites and some background material

While there are no formal prerequisites, we expect a background knowledge equivalent to the Machine Learning course. We also expect you have done a linear algebra and a probability course. If you have doubts about whether this course is for you, please contact the instructor. You can use the following material to pick up the necessary linear algebra and probability background.