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.
Name of the Speaker: Dr. Harpreet S Dhillon
Name of the Speaker: Dr. Harpreet S Dhillon
Name of the Speaker: Dr. M.S. Manikandan
Name of the Speaker: Dr. Harpreet S Dhillon
Deadline for Registration: 19 Jan 2022 20 Jan 2022
Participants: Students, Staff and Faculty Members
Registration Fee: Free (Staff/Faculty)
Online link will be shared to the registered participants
Room 102, School of Electrical Sciences
Indian Institute of Technology Bhubaneswar
Dr. Barathram Ramkumar
School of Electrical Sciences, IIT Bhubaneswar
Email: barathram@iitbbs.ac.in
Dr. M. Sabarimalai Manikandan
School of Electrical Sciences, IIT Bhubaneswar
Email: msm@iitbbs.ac.in
Contact No: Phone: +91-8458047592