Foundational Maths for ML: Statistics
From: ₹500.00
Last date of Registration : 3rd June, 2026
Course description
NEXT AI, Navigating Expert Topics in AI, is a short term academic series designed to provide focused and rigorous learning in both foundational and emerging areas of artificial intelligence. The first two courses in this series, Foundational Mathematics for Machine Learning: Linear Algebra and Foundational Mathematics for Machine Learning: Optimization, were conducted successfully and introduced participants to the essential mathematical principles that underpin modern AI and data driven methodologies.
The upcoming course in the NEXT AI series focuses on statistics for machine learning, which plays a central role in enabling AI and data science models to learn from data, quantify uncertainty, and make informed predictions. The course aims to build a strong foundation in probability and statistics, while highlighting their applications in machine learning. Topics include a probability refresher, concentration inequalities, the Law of Large Numbers and the Central Limit Theorem, parameter estimation, maximum likelihood and maximum a posteriori estimation, as well as linear models such as regression, bias variance trade off, model selection, and regularization techniques including Lasso and Ridge.
Each course in the NEXT AI series is structured as a self contained learning experience. Participants may enroll in any course independently and engage deeply with a specific topic, while progressively building strong conceptual foundations for understanding artificial intelligence and machine learning.
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.
Topics to be covered
Probability refresher
Concentration inequalities
Law of Large numbers, Central limit theorem
Parameter Estimation
Maximum Likelihood estimation, Maximum a posteriori estimation
Linear Models: Regression, Bias Variance trade off , Model Selection
Regularization (Lasso and Ridge)
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. All lectures in this series are conducted online in a synchronous format, encouraging real time interaction and active engagement between participants and instructors. High quality recordings are also provided so that participants can revisit the material later.
Dates of the Workshop : 6th and 7th June, 2026
Mode of the Workshop : Online (Live)
Course duration : 8 hrs
Timings (IST) (Saturday and Sunday) : 02:00 pm to 06:00 pm
Fee for the Workshop
Students and Postdocs: Rs. 500 +18% GST = 590
Faculty and Industry: Rs. 1000 +18% GST = 1180
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.
Learning outcomes
The key learning outcomes of this course include developing a rigorous and conceptually clear understanding of statistics from the perspective of a machine learning researcher or practitioner, along with an understanding of how statistics plays a central role in machine learning. Participants will build strong intuition for how uncertainty is modeled, how data driven inference is performed, and how statistical principles guide learning from finite samples.
Pre-requisites
An elementary introduction to probability will be assumed.
Hands-on component
The coverage of ML applications will be accompanied by demonstrations in python.
Certificate criteria
Attendance is mandatory for getting the certificate
Reviews
There are no reviews yet.