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. The first course in this series, Foundational Maths for Machine Learning: Linear Algebra, was conducted very successfully and introduced participants to the key mathematical ideas underlying modern AI and data driven methods.
The next course in the NEXT AI series focuses on optimization methods used in machine learning, a central component in how many AI and data science models learn from data. This course provides a concise yet rigorous introduction to convex optimization theory from a machine learning perspective. It begins with the fundamental principles of convex optimization and duality, building the mathematical framework needed to understand learning algorithms.
These concepts are then applied to supervised learning methods such as regression and Support Vector Machines. The course also introduces the "kernel trick", a powerful idea that allows linear predictors and classifiers to operate in higher dimensional feature spaces. Through this approach, participants will develop an intuitive and mathematical understanding of how optimization techniques shape modern machine learning methods.
Each course in the NEXT AI series is designed as a self contained learning experience. Participants can join any course independently and engage with a specific topic in depth, while building strong conceptual foundations for understanding AI and machine learning.
Profile of the Instructor
Prof. Pranay is an Assistant Professor in the Centre for Machine Intelligence and Data Science (C-MInDS) at IIT Bombay. Till January 2025, he was a Research Scientist in the Department of Electrical and Computer Engineering at Carnegie Mellon University. In August 2021, he finished his PhD in Electrical Engineering and Computer Science at Syracuse University. Before that, he finished his B.Tech-M.Tech dual-degree in Electrical Engineering from IIT Kanpur. His research interests include federated and collaborative learning, stochastic optimization, reinforcement learning, and differential privacy.
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.
Date of the Workshop : 30th and 31st May, 2026
Mode of the Workshop : Online (Live)
Course duration : 10 hrs
Timings (IST) (Saturday and Sunday) : 09:00 a.m – 02:00 p.m
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
understanding of standard optimization algorithms, such as gradient descent
and stochastic gradient descent (SGD), and how to analyze them under standard
assumptions. These tools are essential for a machine learning researcher or
practitioner. Participants will learn to compare convergence guarantees of
optimization algorithms. Time-permitting, we will also discuss some advanced
topics like federated learning, which have widespread practical applications.
Pre-requisites
It is strongly advised, though not mandatory, that the participants have
taken the previous two modules on linear algebra and optimization.
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 getting the certificate
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