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This course will provide a short but rigorous introduction to convex optimization theory for machine learning. This includes a grounds-up coverage of the basics on convex optimization and duality, which is subsequently applied to supervised learning approaches including regression and SVM. Finally, the course will introduce the “kernel trick,” which will be applied to kernelize regression and SVM, enabling linear predictors/separators in higher dimensions.
Prof. Jayakrishnan U. Nair received a PhD in Electrical Engineering from the California Institute of Technology in 2012 and completed a BTech and MTech in Electrical Engineering from IIT Bombay in 2007. He currently works as a Professor in the Department of Electrical Engineering at IIT Bombay, where he has been serving since June 2014 and also an associate faculty at CMInDS (Centre for Machine Intelligence and Data Science), IIT Bombay. Prior to joining IIT Bombay, he held postdoctoral fellow positions at Centrum Wiskunde and Informatica from June 2013 to May 2014 and at the California Institute of Technology from June 2012 to May 2013.
His primary research interests include queueing theory, communication networks, and heavy tailed phenomena, with a focus on developing analytical frameworks for understanding performance, reliability, and scalability of modern networked systems. His work contributes to both theoretical foundations and practical insights in communication and networked systems.
His research publications have received over 870 citations, with more than 550 citations since 2021. He has an h index of 14 and an i10 index of 20, reflecting sustained contributions and growing impact in his research areas.
Convex Optimization
Duality theory
Application to least squares regression, ridge regression, support vector machine (SVM)
Kernel Functions, how to kernelize SVM, ridge regression
Researchers, Students, Faculty members from other institutes, Industry and Corporate professionals.
A rigorous foundation in convex optimization from the standpoint of an ML researcher/practitioner.
The course will assume familiarity/comfort with calculus at the undergraduate level. Prior exposure (even superficial) to supervised learning, including regression, SVM, etc. would be useful, but not necessary.
Will apply the concepts learned, particularly kernel SVM and kernel regression in real-world supervised learning applications.
Hastie, Trevor, Robert Tibshirani, and Jerome Friedman. "An introduction to statistical learning." (2009).
Mohri, Mehryar, Afshin Rostamizadeh, and Ameet Talwalkar. Foundations of machine learning. MIT press, 2018.
Attendance and MCQs are mandatory for certification
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