Course description
NEXT AI, Navigating Expert Topics in AI, is a short term academic series designed to deliver focused learning on core and emerging areas of artificial intelligence. All lectures in this series are conducted online in a synchronous format, encouraging real time interaction and active engagement between participants and instructors, with high quality recordings provided for later reference. The first course in this series is Foundational Maths for Machine Learning: Linear Algebra, which sets the mathematical groundwork required for understanding modern AI and data driven methods. This course offers a clear and structured introduction to Singular Value Decomposition, emphasizing both its mathematical foundations and its broad applications in data science and machine learning. It is designed to be accessible to learners with no prior background in eigenvalues or eigenvectors, while a basic familiarity with finite dimensional vector spaces will be helpful. The material is developed carefully from first principles, enabling participants to build strong intuition alongside formal understanding. This course will also include python-based demonstrations of SVD/PCA applied to regression, unsupervised learning, and an application to human facial identification.
Profile of the Instructor
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.
Session Details
Throughout the course, subject related questions and conceptual doubts are addressed directly by the course instructor and teaching assistants, ensuring continuous academic support. Assistance for enrolment procedures and other non technical queries is provided through the NPTEL+ platform.
Date of the Workshop : 7th and 8th March, 2026
Mode of the Workshop : Online (Live)
Course duration : 8 hrs
Timings (IST) (Saturday and Sunday) : 02:00 pm to 06:00 pm
Who May Benefit
Building a strong foundation that seamlessly progresses to advanced topics in Artificial Intelligence, these courses are designed to support learners at every stage of their journey. These short term courses offered by CMInDS IIT Bombay are designed to cater to academic institutions, research centers, and industry and corporates, serving researchers, students, faculty members from other institutes, and industry and corporate professionals seeking to enhance their technical and analytical skills. These short term courses also address the AI needs of industries and corporates, especially professionals in Data Science and AI who are looking to strengthen their fundamental understanding while gaining deeper expertise in specialized areas.
Key Learning outcomes
The key learning outcomes of this course include developing a rigorous and conceptually clear understanding of Singular Value Decomposition and Principal Component Analysis, grounded in linear algebra rather than heuristic explanations. Participants gain strong intuition for how these techniques work, why they are effective, and when they should be applied in data science and machine learning workflows, particularly for dimensionality reduction, noise filtering, feature extraction, and model interpretation. The course also emphasizes the connection between theory and practice through guided hands-on sessions, enabling learners to confidently implement SVD and PCA in real world scenarios. In addition, this course provides an essential mathematical foundation for prospective students who plan to pursue postgraduate studies or a PhD in machine learning, data science, or related areas, helping them transition smoothly into advanced coursework and research.
Pre-requisites
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.
Hands-on component
Will apply the concepts learned, particularly kernel SVM and kernel regression in
real-world supervised learning applications.
Textbooks/References
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.
Certificate criteria
Attendance is mandatory for participation certification
Devanshu Kashyap –
The course is important in current times but highly priced for industry category and also condensed too much into 8 hours. A lower cost and week-wise distribution in LMS video format would be extremely helpful. Live sessions for doubt clearing in weekend would be a much better and cost efficient format.