IIT-Bhubaneswar To Conduct Indo-USA Short-Term Online Course From Jan 21; Check Details
Bhubaneswar: IIT-Bhubaneswar will conduct an Indo-USA online short-term course on ‘Application of Machine Learning to Wireless Communication’ from January 21 to 24.
Sponsored by Scheme for Promotion of Academic and Research Collaboration (SPARC), under MHRD, Government of India, applicants from IIT-Bhubaneswar as well as outside can register for the course for free by visiting the official website of the institute.
According to the institute, the course provides a brief introduction to machine learning that is tailored for communication and information theory researchers. Experts opined that though communication systems had traditionally relied on developing a mathematical model and producing optimized algorithms for that particular model, the data-driven approach based on machine learning has gained popularity in recent years with increasing access to data and computing resources.
Undergraduate, post-graduate, research students, academicians, doctors, researchers and engineers from Industries and R&D organizations are eligible for taking up the course. “The first module of the course 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,” the authorities of the institute stated.
Similarly, the second module will focus on statistical estimation. On the first day, Dr Harpreet S Dhillon, from Bradley Department of Electrical and Computer Engineering Virginia Tech, USA, will deliver an introduction to machine learning and its role in communications; statistical estimation and its role in machine learning and determinantal learning for subset selection in wireless networks.
The folloiwng day, he will present the case study on Grassmann Clustering in Massive MIMO. On the third day, Dr M S Manikandan of IIT-Palakkad will deliver on Cognitive radio applications. The course will conclude with Dhillon’s lecture on density estimation using GMM and expectation-maximization, among other topics.
