CSE Department Special Topics courses in 2022 Holi Semester

In the Holi Term 2022, the CSE Department is offering a very large number of Special Topics Courses. These may not have listed pre-requisites in the Courses of Study, but it is expected that if you wish to register for any of these courses, you will have the necessary background and maturity. In particular, these courses are meant for those seeking to do research (PhD, MS Research, and M Tech level students), so the expectation will be that you have competed 120 earned credits.

All students wishing to register for any Special Topics Courses are required to fill the following form: Choice for Special Topics Courses (Holi Term 2022)

Students are not expected to register for more than 1 special topics course. An exception is made for those who are applying for a specialisation, in which case at most 2 courses are permitted. Instructors reserve the right to deregister those who do not fill this form. Also, if you are opting for a Special Topics course, you will be required to take it for credit (exception given only to PhD students), and you will be expected not to withdraw (the only concession being on grounds of medical or family emergencies).

COL861 Special Topics in Hardware Systems

Instructor: Prof. M. Balakrishnan + (Being an online course I plan to invite other researchers for some of the specific topics)

About the course: Processor, custom hardware, firmware and software represent one continuum today - driven by the goals of obtaining highest system performance. With the massive growth in embedded devices very often comparison between competing solutions is not just on speed (mips, mflops, fps etc) but more critically on performance per watt. In this course we would cover the advances primarily from hardware viewpoint to meet these objectives both at component level (e.g. CPU, Memory etc) as well as at system level (e.g. Accelerators).

Prerequisites: Two basic courses in digital circuits/systems and computer architecture/organization

Who can benefit: Anyone who is interested in understanding the big picture of system performance in relation to advances in architecture, technology as well as design process

Detailed course outline: https://www.cse.iitd.ernet.in/~mbala/Teachings/Current/COL861.html

COL863 Special Topics in Theoretical Computer Science: Rapid Mixing in Markov Chains

Instructor: Prof. Amitabha Bagchi

Course objectives: The purpose of this course is to discuss the mathematical tools and techniques required for theoretically analysing the time taken for a Markov Chain to converge to its steady state distribution.

Background required: Elementary probability, some graph theory, linear algebra.


Basic properties of Markov Chains; Some useful Markov Chains; Random walks; Markov Chain Monte Carlo (including Metropolis and Glauber dynamics chains); Total Variation Distance and Mixing; Coupling; Strong stationary times; Random walks on networks; Hitting times and Cover times; Algebraic view of Markov chains; the Transportation Metric and Path Coupling.


Course outline

All Chapter numbers refer to LPW. The bullet points in italics will be largely self-study.

Refresher texts

Course webpage: https://www.cse.iitd.ac.in/~bagchi/courses/COL863_21-22/

COL864 Special Topics in Artificial Intelligence: Planning and Estimation for Autonomous Systems

Instructor: Prof. Rohan Paul


Planning and estimation are central to modern autonomous systems. This course will cover the concepts, principles and methods for intelligent decision-making with imperfect or uncertain knowledge. Students will develop an understanding of how different planning and learning techniques are useful in problem domains where robots or other embodied-AI agents are deployed. Previous coursework in artificial intelligence or machine learning is required.

Topic list (tentative)

Course Components

Minor and major exams. Programming assignments (tentatively 1-2). Study of a contemporary works in planning and learning technique relevant to autonomous systems (details in due course).


Introduction to Artificial Intelligence (COL333-671) or Introduction to Machine Learning (COL774 or equivalent). Programming proficiency and knowledge of probabilistic models, basic deep learning, basic search algorithms, logic and probability will be an advantage.

Learning outcomes

At the end of the course students will model a robotic system (e.g., a ground robot or manipulator) as a decision- making AI agent. Students will be able to formulate/solve relevant planning and estimation problems in this domain and understand how incorporate recent learning-based methods decision-making algorithms.

Other Information

This course will focus on AI aspects of autonomous systems. A robotic system (ground/air vehicle or manipulator) will be modeled as an AI agent capable of sensing and taking simple actions in the environment. The detailed control/physical aspects of the system will be abstracted to a certain degree in the course. In future offerings experimental component with a real system is likely to be added but is beyond scope in the current offering.


COL865 Special Topics in Computer Applications: Social Computing

Instructor: Prof. Abhijnan Chakraborty

Course content

  1. ​ Social network analysis
  2. Fundamentals of Social Data Analytics
  3. Social search and recommendation algorithms
  4. Crowdsourcing
  5. Harmful users/content on social media – hate speech, fake news, spammers in social networks, etc.
  6. Different types of social platforms – Anonymous social networks, E-commerce sites
  7. Basics of computational social choice — voting, allocation algorithms
  8. Social media and digital health


Course objectives

COL866 Special Topics in Algorithms: Algorithmic Game Theory

Instructor: Prof. Naveen Garg


  1. Introduction to Algorithmic Game Theory
  2. Definitions of Games and Equilibria; Two player Zero-Sum Games; Proof of Nash Equilibria using Linear Programming Duality.
  3. Auctions and Optimal Mechanism Design, Myerson’s lemma, VCG auctions
  4. Price of Anarchy in network routing games, Atomic routing games, Potential function games
  5. Hierarchy of Equilibria, Best-case and strong Nash Equilibria, Best-response dynamics and no-regret dynamics, Pure nash equilibria and PLS-completeness
  6. Arrow’s and Gibbard-Satherthwaite thms

Supplementary reading materials:

Course webpage: https://www.cse.iitd.ac.in/~naveen/courses/CSL863/index.html

COL870 Special Topics in Machine Learning: Meta-Learning

Instructor: Dr. Gautam Shroff (Adjunct Faculty)

Overview: ‘Meta-Learning’ or ‘learning to learn’ are machine-learning/deep-learning techniques which, by experiencing many different learning tasks, can adapt to new tasks/domains/environments, either very “rapidly”, i.e., requiring very little new data/experience, or in a manner robust to distributional shifts, such as when users change their behaviour, as has been experienced across the board due to the pandemic. Both scenarios are also closely related, since distributional shifts also imply the relative scarcity of ‘new’ data, with practical considerations making it imperative to rapidly adapt to every ‘new normal’. Meta-learning is also closely related to two long-desired goals of AI in general, viz., continual learning (without forgetting) and learning higher-level (and ideally causal) representations that allow for better generalisation, e.g. to deal with non-stationarity or for longer duration forecasting etc.


Basics: Introduction. (i) the course will aim to cover the basic techniques for meta-learning (ii) outline and model selected industry applications in a meta-learning setting and (iii) highlight the connections with closely related long-term AI-research goals.

Course webpage: https://sites.google.com/view/meta-learning-2021/home

COL873 Special Topics in Natural Language Processing

Instructor: Prof. Mausam


It is strongly recommended that the student has completed COL772 or has knowledge commensurate to a student who has.


This will primarily be a paper reading and student presentation-driven course. The goal will be to learn about the state the art in NLP, learn how to read and critique research papers, try out research ideas, and write survey articles.


Some topics that will be covered include:

The midterm will require you to write a survey paper, and an open-book exam based on topics covered in the course. We will do an assignment and a project.

SIV813 Applications of Computers in Medicines

Instructor: Prof. Kolin Paul


We will look at the types of wearable devices and sensors in use today. We will consider the impact of the miniaturisation of implantable devices, and the challenges around systems’ use of big data on healthcare systems. We will also study how digital devices in healthcare are designed and regulated.