CS 726: Advanced Machine Learning (Spring 2014)

Lecture Schedule Slot 9, Monday, Thursday: 3:30--5:00
Venue SIC 201, KR Building, CSE Department.
Instructor: Sunita Sarawagi
TA: Ahbirut Gupta and Manasa Gootla
Instructor's office hours check here
Syllabus and week-wise calendar: Click here
Homeworks Click here
Previous years' exams

Course description

This course will be primarily about building supervised and unsupervised learning models involving a very large number of unknowns. The core techniques will be from two primary areas in machine learning: probabilistic graphical models and learning in structured output spaces. Topics include:


Foundation in machine learning (CS 725) or equivalent (example, CS 419).


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 book

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

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

Survey articles