CS 726: Advanced Machine Learning (Spring 2018)
Lecture Schedule Slot 1, Monday: 8:30--9:25, Tuesday: 9:30--10:25, Thursday: 10:35--11:30
Venue CC 103
Instructor: Sunita Sarawagi
TA: Ujjwal Jain,
Sandeep Subramanian, Vertika Srivastava,
Instructor's office hours check here
Syllabus and week-wise calendar: Click here
Homeworks Click here
This course will concentrate on modeling, generation, and prediction of multiple inter-dependent variables. The topics covered will span probabilistics graphical models (directed and undirected), inference methods like junction trees, belief propagation, and other approximate methods, sampling methods like MCMC, variational auto-encoders, GANs, neural architectures for sequence and graph-structured predictions. When appropriate the techniques will be linked to applications in translation, conversation modeling, speed recognition, graphics, and science.
725 or an equivalent introductory course on machine learning. The
course assumes basic knowledge of probability, statistics, and linear
algebra. 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
and deep learning, for example, as covered in Chapter 6 of the same
A quiz will be administered within the course drop deadline to help
you assess if you have the requisite background for the course.
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.
Approximate credit structure
Audit students have to score more than 30% over all, including
assignments, quizzes and mid/end sem exam.
- 25% Mid-semester exam
- 25-30% End semester exam
- 12% Graded paper homeworks
- 10% Quizzes
- 13-18% Project + programming homework
Primary text books
Probabilistic Graphical Models: Principles and Techniques,
by Daphne Koller and Nir Friedman, MIT Press, 2009.
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
A Primer on neural networks for natural language processing, by Yaov Goldbeg.
- R. G. Cowell, A. P. Dawid, S. L. Lauritzen and D. J.
Spiegelhalter. "Probabilistic Networks and Expert Systems".
M. I. Jordan (ed). "Learning in Graphical Models". MIT Press. 1998.
Collection of papers. These appear collated here.
J. Pearl. "Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference." Morgan Kaufmann. 1988.
- Graphical models by Lauritzen, Oxford science publications
- F. V. Jensen. "Bayesian Networks and Decision Graphs". Springer. 2001.
- Neural Networks and Deep Learning by Michael Nilson