CS 726: Advanced Machine Learning (Fall 2016)


Lecture Schedule Slot 1, Monday: 8:30--9:25, Tuesday: 9:30--10:25, Thursday: 10:35--11:30
Venue SIC 201, KR Building, CSE Department.
Instructor: Sunita Sarawagi
TA: Srijay Deshpande, Lakshya Daksh, Rahul Mitra
Instructor's office hours check here
Syllabus and week-wise calendar: Click here
Homeworks Click here

Course description

This year the advanced machine learning course will be divided into two parts:

Prerequisites

The course assumes basic knowledge of probability and statistics and linear algebra. A quiz will be administered within the course drop deadline to help you assess if you have the requisite background for the course. Chapters 2 and 3 of the Deep-learning book are a good place to refresh the necessary required background. Also, the course assumes basic background in machine learning, for example as covered in Chapter 5 of the Deep-learning book.

Eligibility

The course is open to CS MTechs, PhD, DD and BTech students. Students of other departments should approach for permission only if they meet the necessary pre-requisites. Third year BTech students need to take prior permission from the instructor for enrolling in the course.

Credit/Audit Requirements

Approximate credit structure

Audit students have to score more than 30% over all, including assignments, quizzes and mid/end sem exam.

Reading List

Primary text books

Probabilistic Graphical Models: Principles and Techniques, by Daphne Koller and Nir Friedman, MIT Press, 2009.

Deep Learning by Ian Goodfellow, Yoshua Bengio and Aaron Courville, MIT Press, 2016.

The course calendar will provide links to other relevant papers and book chapters for specific topics.
Other relevant text books:

Online courses on deep learning