IISc Certification | 9 Months | Campus Visit |

1. Robolution is the future

According to PwC, AI, robotics, automation, and autonomous machines will drive 70% of GDP growth between now and 2030 globally.

  • "The need for skilled engineers who can build, run, and maintain AI systems to go up by 20-50 million globally by 2030." 1
  • "50% of Indian firms to deploy intelligent automation by 2024." 2
  • "The autonomous navigation market to reach $13.5 Bn by 2030." 3

2. High Impact Format

  • Live Interactive Sessions With expert faculty from IISc
  • Experiential Learning Supported by use-cases and capstone projects
  • Hi-Tech Hi-Touch Learning with iPearl.ai Access best-in-class digital content for 24x7 learning
  • Campus Visit Experience IISc campus

3. IISc Advantage
World’s #1* Research University


IISc (Indian Institute of Science) is the oldest and the finest higher education institute of its kind in India. It pursues excellence in research and education in several fields of Science and Engineering and is one of the first three publicly funded institutes to be awarded the Institute of Eminence status. The alumni of IISc hold significant academic and industry positions around the globe. For more information visit www.iisc.ac.in

The PG Level Advanced Certification Programme on Machine Learning and AI for Autonomous Systems will be delivered by IISc’s Centre for Continuing Education (CCE). CCE delivers courses suitably designed to meet the requirements of various target groups - research & development (R&D) laboratories and industries, research scientists, and engineers, and enable them to grow into competent managers of technology-intensive and data-driven organizations. For more information, visit cce.iisc.ac.in

Expert Faculty

Taught by eminent IISc Faculty Group

Research Expertise: Statistical Machine Learning, Reinforcement Learning, and Deep Learning in Low data Regimes.

  • Heads the Statistics and Machine Learning Group, CSA Department, IISc.
  • Worked as a postdoctoral researcher with EURANDOM, The Netherlands.
  • Published various research articles on Deep Learning, Algorithmic Algebra, and Stochastic Optimization topics.
  • An IIT Madras and IISc alumnus, he also teaches a few other programs offered by the Centre for Continuing Education (CCE) at IISc.

Research Expertise: Stochastic Approximation Algorithms, Stochastic Control, Reinforcement Learning, Simulation Optimization, Autonomous Systems, Vehicular Traffic Control, Smart Grids, Communication/Wireless Networks

  • Head of Stochastic Systems Laboratory at IISc.
  • Authored research papers and a book titled ‘Stochastic Recursive Algorithms for Optimization: Simultaneous Perturbation Methods.’
  • Worked as a Principal Investigator for sponsored projects of organisations like DRDO, Xerox, DST - GoI, and Texas Instruments (India).
  • Winner of Prof. Satish Dhawan Young Engineer Award (2013), Rajib Goyal Young Scientist Prize (2012,13,15), and more such honors.
  • Besides IISc, worked as a Visiting Faculty for leading institutions like IIT Delhi, INRIA - France, TU Delft, Netherlands, and NUS - Singapore.

Research Expertise: Hybrid robotic Systems, Safety Critical Control, Application of Learning Theory to Legged Locomotion

  • Principal Investigator for IISc’s Stochastic Robotics Lab, where novel learning-based controllers for quadrupeds are developed.
  • Started career as an R & D Engineer with Tejas Networks, Bangalore.
  • Worked as a Postdoctoral Fellow for California Institute of Technology (Caltech), USA.
  • His work is majorly focused on robotics, especially in the domain of legged robots.
  • His research and conference publications are mostly centered around Bipedal Robotic Locomotion.


With the state-of-the-art curriculum, designed by IISc Faculty, gain an insight into AI implementation in the modern context of autonomous systems with the possibility to apply the learnings to real-world tasks.

Module 0: Bridge Module

  • Basics of Probability
  • Basics of Linear Algebra

Module 2: Introduction to Sequential Decision Making and Reinforcement Learning

  • Introduction and Real world examples
  • Sequential Decision Making under Uncertainty: Multi-armed Bandits
  • Markov Decision Process, Value & Q-functions, Finite and Infinite Horizon Problems
  • Value and Policy iteration, Dynamic ProgramingMarkov Decision Process, Value & Q-functions, Finite and Infinite Horizon Problems
  • Policy evaluation with Monte-Carlo and Stochastic approximation

Module 3: Data-driven Optimization

  • Basics of Optimization
  • Deterministic Algorithms for Local Search-Gradient/Newton/Jacobi Schemes
  • Data-driven Gradient Search Algorithms for Optimization – Kiefer-Wolfowitz Algorithm, Random Perturbation Methods, Smoothed Functional Schemes
  • Data-driven Newton-based Random Perturbation Algorithms for Multi-Dimensional, Parameter Optimization
  • Data-driven Algorithms for Discrete Parameter Optimization with Noisy/Uncertain Objectives
  • Data-driven Algorithms for Optimization under Constraints
  • Case studies in Dynamic Pricing and Resource Allocation, Vehicular Traffic Control, Wireless Networks

Module 4: Reinforcement Learning Algorithms

  • Model-free Algorithms for Policy Evaluation – Incremental Update Temporal Difference Learning, N-step Temporal Difference Learning
  • Model-free Least Squares Temporal Difference Learning, Least Squares Policy Evaluation, Incremental Least Squares Temporal Difference Learning
  • Model-free Value-based Algorithms for Control – SARSA, Q-learning, Expected SARSA, Double Q-learning
  • Algorithms for On-policy Prediction with Value Function Approximation – Linear Approximation Architectures (Fourier/Polynomial bases, Tile Coding, Radial basis Functions), Non-Linear Approximation Architectures (Artificial Neural Networks)
  • Off-policy Value-based Algorithms with Approximation – Semi-Gradient Methods, Gradient descent in Bellman error, Gradient Temporal Difference Learning, Temporal Difference with Correction
  • Policy Gradient Algorithms – Policy Parameterization, REINFORCE: Monte-Carlo Policy Gradient, REINFORCE with Baseline, Actor-Critic Algorithms
  • Recent Algorithms - Deep Q-network, Trust Region Policy Optimization, Proximal Policy Optimization, A2C, A3C

Module 5: RL Applications to Autonomous Systems

  • Deep RL Demonstrations on Simulated Environments
  • Introduction to Robotics

Is this programme for me?

  • Yes, if you are a tech professionals involved in the design and validation of autonomous systems across industries.


  • Qualification: B.Tech (BE) degree in any engineering discipline with min. 50% marks or at least 4 years of job experience in the related field.
  • Coding Experience: Required, preferably in Python
  • Good to have: Exposure to Engineering Mathematics

How can I enrol in this programme?

  • Application

    Apply for the

  • Upload Document


  • Selection

    Selection by
    IISc Committee*

  • Enrollment

    Join the

*Selection for the programme will be done by IISc and is strictly based on the education, work experience, and motivation of the participants.

**Scanned copies to be submitted within 7 days 1. Education Certificate 2. Experience Letter/Latest Pay Slip

What is the return on my investment?

By successfully completing the programme, you will be able to shift to new roles that require expertise in autonomous systems powered by AI and become adept at managing and scaling intelligent automation initiatives in your organisation.

  • $9.32 Bn would be the global autonomous navigation market by 2028 4
  • 97 Mn new roles will be created by 2025 as humans, machines and algorithms increasingly work together 5
  • 90% of organisations expect AI to increase their workforce capacity 6
  • 90% of enterprises will have an automation architect by 2025 7
  • $3 Tn would be the worldwide economic impact of converged AI-powered automation by the end of 2022 8
  • $15.7 Tn would be the potential contribution to the global economy by 2030 from AI 9

Programme Fee

Details 9 Months Programme
Programme Fee* ₹2,80,000/-

12-Month 0% EMI available Nominate your employees to avail special benefits *GST as applicable

(i) Application Fee of ₹2,000, and
(ii) Campus visit fee will be based on actuals and to be borne by the participants
(iii) Fees paid are non-refundable and non-transferable.

Frequently Asked Questions

The programme is useful for tech professionals interested in learning AI/ML and reinforcement learning to design and build autonomous systems for applications across industries.

You will learn from eminent IISc faculty who hold academic and research credentials from the world’s best institutions. Meet your faculty here.

About TalentSprint

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Established in 2010, TalentSprint is a part of NSE group and a global edtech company that brings transformational high-end and deep-tech learning programs to young and experienced professionals. The company’s digital learning platform ipearl.ai offers a hybrid onsite/online experience to seekers of deep technology expertise. TalentSprint partners with top academic institutions and global corporations to create and deliver world class programs, certifications, and outcomes.Its programs have consistently seen a high engagement rate and customer delight. It is a leading Innovation Partner for the National Skill Development Corporation, an arm of the Ministry of Skill Development and Entrepreneurship, Government of India. A recipient of various prestigious accolades, TalentSprint was recently honored with the Indian Achievers Award 2022, for its excellence in building deeptech talent in India. For more information about TalentSprint, visit TalentSprint website.