Advances in modern computing technologies have led to major breakthroughs in Artificial Intelligence (AI) and Machine Learning (ML). Organisations across the world are searching for nimble and effective ways to leverage AI and ML for automation, better decision-making, and competitive advantage. In India, too, a PwC survey of Indian businesses found that 74% companies believed AI will improve economic growth, cyber security, global health, and education.

The Indian Institute of Technology Bombay has designed the Certificate Program in Machine Learning & AI to answer the growing need to upskill individuals in AI and ML. This weekend program, conducted over 6 consecutive weekends via live online lectures will unravel the emerging technology and provide you with the requisite toolkit to deploy it competently.

Carefully conceptualised and planned, this program will benefit software engineers, programmers and other IT professionals with at least one year of programming experience. Participants will learn how to use key machine learning algorithms to build powerful predictive and prescriptive models and will develop an in-depth understanding of popular methods like classification and regression, optimisation techniques, neural networks, decision trees, agent-based models and deep learning. The learning journey includes live online lectures, real-life case studies, group discussions, simulations and interactions with industry experts.


  • Look past jargon of ML & AI and understand their use and applications across industries.
  • Understand methods like classification and regression, optimisation.
  • Build predictive models using neural networks and decision trees.
  • Use classification and regression models to solve problems.
  • Improve the accuracy of machine learning model predictions.
  • Differentiate between optimisation techniques and how they solve learning problems across models, minimise errors or maximise rewards.
  • Implement NLP to build text classification systems using both linear classifiers and deep learning methods.
  • Build AI using multi-agent-based models that run search algorithms and achieve their tasks.


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    Coming Soon


    6 Weeks
    (6 Consecutive Weekends, Live Online Lectures)


    INR 76,500 + GST

Who is this Program For

  • The applicants should be graduate with at least a year of work experience in coding
  • Students must be comfortable with high-school probability and calculus. Prior experience in coding with Python will be beneficial

Program Modules & Faculty


Introduction to AI/ML

  • Brief History
  • Recent Surge, Topics and Applications
  • Course Overview

Supervised Learning Module 1

  • Supervised Learning Problem
  • Linear Models for Classification and Regression
  • Feature Design

Supervised Learning Module 2

  • Generalization
  • Overfitting
  • Cross–Validation
  • Regularization
  • Non-linear Models

Guest Speaker #1
Practical Learning

  • Linear Models for Classification and Regression

Supervised Learning Module 3

  • Neural Networks
  • Back Propagation Algorithm

Supervised Learning Module 4

  • Decision Trees
  • Support Vector Machines
  • Nearest Neighbour Method

Optimization Module 1

  • What is Optimization
  • Elements of Optimization Problem
  • Continuous and Discrete Variables
  • Classifying and Specifying an Optimization Problem

Guest Speaker #2
Practical Learning

  • Neural Networks
  • Hyperparameter Tuning

Optimization Module 2

  • Linear Programming (LP)
  • Standard Form
  • Slack Variables
  • Integer Linear Programming (ILP)
  • Solving LPs and ILPs

Optimization Module 3

  • Convex Optimization
  • Robust Optimization
  • Minimax Optimization
  • Data Driven Optimization and Scenario Approach

Multi-Agent System Module 1

  • Game Theory
  • Introduction to Basic Ideas
  • Examples of Games, Prisoner’s Dilemma
  • Coordination, Hawk Dove

Guest Speaker #3
Practical Learning

  • Linear Programming (LP)
  • Integer Linear Programming (ILP)

Agent-Based Models 1

  • Introduction to Agent-Based Models (ABM)
  • How to Build ABM
  • Types of Agents
  • Machine Learning and Adaptive Agents

Agent-Based Models 2

  • Financial Markets as ABMs
  • Artificial Stock Markets
  • The Santa Fe Artificial Stock Market

Multi-Agent System Module 2

  • Minority Games and Financial Markets
  • Using Game Theory for Decision Making
  • Examples of Game Theory for Deployed Systems

Guest Speaker #4
Practical Learning

  • Building ABMs Using NetLogo

Natural Language Processing Module 1

  • What is NLP
  • Feature Representations
  • Introduction to Language Modeling
  • Basic Text Classification using Traditional Methods and Deep Learning

Natural Language Processing Module 2

  • Modern Tools for Word Embeddings
  • Sentiment Analysis
  • Sequence to Sequence Problems and General Architectures to Solve Them

Online Learning

  • Exploration and Multi-Armed Bandits
  • Regret Minimization
  • Epsilon Greedy, UCB
  • Thompson Sampling Algorithms

Guest Speaker #5
Practical Learning

  • Implementation of a Text Classification Using Linear Classifiers and Deep Learning


  • Depth First Search
  • Breadth First Search
  • Uniform Cost Search
  • Heuristics
  • Consistency
  • A* Approach

Evolutionary Algorithms

  • Evolutionary Algorithms as an Instance of Local Search
  • Cross-Entropy Method
  • CMA-ES
  • Applications

Reinforcement Learning

  • Reinforcement Learning

Guest Speaker #6
Practical Learning

  • Implementation of Search Algorithms


Prof. Shivaram Kalyanakrishnan
Prof. Shivaram Kalyanakrishnan

Program Coordinator

Associate Professor in the Department of Computer Science and Engineering at Indian Institute of Technology Bombay

Prof. Shivaram Kalyanakrishnan’s research interests include artificial intelligence and machine learning, spanning topics such as sequential decision making, multi-agent learning, on-line learning, and humanoid robotics. Kalyanakrishnan received a Ph.D. in computer science from the University of Texas at Austin. Subsequently he was a Research Scientist at Yahoo Labs Bangalore and an INSPIRE Faculty Fellow at the Indian Institute of Science, Bangalore. His contributions to robot soccer have received two Best Student Paper awards at the annual RoboCup competitions. Kalyanakrishnan was a member of the first study panel of the One Hundred Year Study on Artificial Intelligence (AI100), which in 2016 released its report titled “Artificial Intelligence and Life in 2030”.

Prof. Ankur Kulkarni
Prof. Ankur Kulkarni

Program Coordinator

Associate Professor at Indian Institute of Technology Bombay

Prof. Ankur Kulkarni received his B.Tech. from IITB in 2006, M.S. in 2008 and Ph.D. in 2010, both from the University of Illinois at Urbana-Champaign (UIUC). His research interests include game theory, stochastic decision theory, information theory, optimization and operations research. He has made fundamental contributions to game theory, team theory and issues of information in dynamic decision-making. He was an Associate (from 2015–2018) of the Indian Academy of Sciences, Bangalore, a recipient of the INSPIRE Faculty Award of the Department of Science and Technology, Government of India, 2013, Best paper awards at the National Conference on Communications, 2017, Indian Control Conference, 2018, International Conference on Signal Processing and Communications (SPCOM) 2018, Excellence in Teaching Award 2018 at IITB and the William A. Chittenden Award, 2008 at UIUC. He was a consultant to the Securities and Exchange Board of India on regulation of high frequency trading and is presently a consultant to HDFC Life Insurance Co. He has been a visiting faculty at MIT (USA), University of Cambridge (UK), NUS (Singapore) and IISc Bangalore.

Prof. Preethi Jyothi
Prof. Preethi Jyothi


Assistant Professor in the Department of Computer Science and Engineering at Indian Institute of Technology Bombay

Prof. Preethi Jyothi’s research interests include machine learning applied to speech and language (with a focus on Indian languages) and multimodal learning applied to text and videos. Before moving to IITB in 2016, she was a Beckman Postdoctoral Fellow at the University of Illinois at Urbana-Champaign and she obtained her Ph.D. in computer science in 2013 from The Ohio State University where she was awarded a University Fellowship. She received a 2017 Google Faculty Research Award for research on accented speech recognition. Her work on statistical learning methods for pronunciation models and probabilistic transcriptions received Best Student Paper Awards at Interspeech 2012 and ICASSP 2016, respectively.

Program Fee

Dates Length Location Tuition Fees
Coming Soon 6 Weeks Live Online Lectures
Saturday - 09:00 AM to 12:00 PM
Sunday - 03:30 PM to 06:30 PM
INR 76,500 + GST
Deadline Application Fee
Round 1 Feb 07, 2020 -
Round 2 Mar 06, 2020 -
Round 3 Jun 25, 2020 -
Remarks Instalment Amount
Instalment 1 Within 7 days of selection INR 38,250 + GST
Instalment 2 10 days prior to the course start date INR 38,250 + GST

Student Loan Details

We currently do not offer Student Loans for this program. However, we are working on providing an option and will alert you as soon as it is made available.