From: ₹1,600.00
This short course will provide a formal introduction to Singular Value Decomposition (SVD), and demonstrate its applications in data science and machine learning. The course will assume no prior background in eigenvectors/eigenvalues, though a basic background in the theory of (finite
dimensional) vector spaces would be useful.
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. 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.
| 1 | Introduction to row, column spaces of a matrix |
| 2 | Fundamental Theorem of Linear Algebra |
| 3 | Singular Value Decomposition (SVD) |
| 4 | Low rank matrix approximations via SVD |
| 5 | Linear regression, ridge regression, bias-variance tradeoff |
| 6 | Principal components regression |
| 7 | Principal Components Analysis (PCA), unsupervised learning |
| 8 | Application: Eigen Faces |
Researchers, students, faculty members from other institutes, and industry and
corporate professionals.
A rigorous understanding of SVD, PCA
Intuition for how and when to apply SCD/PCA in ML applications
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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