CS 791 was earlier called CS 726 (Advanced ML). The new name is to more faithfully reflect the contents of the course. Student who have already taken CS 726 should not take this course. It will be considered a duplicate.
Eligibility
The course is open to PhD, Masters, DD and BTech students provided they have taken an introductory course in IITB in machine learning (listed below) and obtained at least a BC grade in it.
You are required to have taken a
formal introductory ML course at IITB like CS 725 or CS 217 or CS 419 or DS 303 or equivalent courses in other departments. Online ML courses do not qualify as pre-requisites.
The course assumes basic knowledge of probability, statistics,
linear algebra, and numerical optimization. Chapters 2, 3, 4 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 and deep learning, for example, as covered in Chapter 6
of the same book. Further, we will assume that students are
familiar with CNNs, RNNs, and sequence to sequence learning with
attention. Also, there will be programming assignments that will benefit from fluency in Python.
A quiz will be administered within the course drop deadline to help
you assess if you have the requisite background for the course. You may be asked to drop the course if you score below a threshold in that quiz.
Approximate credit structure
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.