Harpreet S. Dhillon - Machine Learning in Communications

Short Course on Machine Learning in Communications

Prof. Dhillon delivered a short online course on Machine Learning in Communications in the JTG/IEEE ITSoc Summer School in Information Theory, Signal Processing, Telecommunication, and Networking held at IIT Kanpur from June 28 - July, 1 2021. Video recordings of all his lectures are available on YouTube. The slides are available here.

The next offering of this course was at IIT Bhubaneswar from January 21 to January 24, 2022. (Event Link)

Course Summary

Communication system design has traditionally relied on developing a mathematical model and producing optimized algorithms for that model. However, with the increasing access to data and computing resources, a complementary data-driven approach based on machine learning has gained interest in recent years. This short course provides a brief introduction to machine learning that is tailored for communication and information theory researchers. The first module will provide an overview of statistical learning that will lead into the discussion of the types of communication system design problems that can benefit from machine learning. A case study exploring the connection of machine learning to point processes in the context of subset selection problems in wireless networks will also be presented. The second module will focus on statistical estimation. Popular supervised learning algorithms will be interpreted as ML and MAP estimators of appropriate underlying statistical models. The last two modules will focus on unsupervised learning, including discussions on k-means, expectation maximization, as well as detailed case studies related to distributed learning and codebook design in MIMO systems.


  • Introduction to Statistical Learning

  • Role of Machine Learning in Communications

  • Determinantal Learning for Subset Selection in Wireless Networks

  • Statistical Estimation

  • Supervised Learning: Introduction, Interpretation as ML/MAP Estimators

  • Unsupervised Learning: Introduction, k-means, and Expectation Maximization

  • k-means Clustering on a Grassmann Manifold for MIMO Codebook Design

  • Distributed Learning in Wireless Networks

Video Lectures

Lecture 1: Machine Learning Basics

Lecture 2: Role of Machine Learning in Communications

Lecture 3: Statistical Estimation and its Role in Machine Learning: Part I

Lecture 4: Statistical Estimation and its Role in Machine Learning: Part II

Lecture 5a: Theory-Guided Machine Learning in Communications

Lecture 5b: Introduction to Unsupervised Learning

Lecture 6: Case Study: Grassmann Clustering in Massive MIMO

Lecture 7: Density Estimation using GMM and Expectation Maximization

Lecture 8: Gradient Compression for Federated Learning: A Wireless Perspective