Frequently Asked Questions

Autonomous systems, robots, and AI are transforming business and society, boosting productivity and driving economic growth.

In a nutshell, autonomous systems are systems that can accomplish a task, achieve a goal, or interact with its surroundings with minimal to no human involvement. Some examples include self-driving cars, UAVs, smart manufacturing robots, care robots for the elderly, and virtual agents for training or support.

A study by Forbes reports that AI/ML, robotics, and autonomous machines are expected to drive 70% of GDP growth between now and 2030 globally. Further, the report also states that the massive adoption of autonomous systems could soon transition superpowers like America into a machine economy.

To fully move and embrace this intelligent system's future, companies need a skilled workforce to support these initiatives and help the organisation prepare for the future.

The PG Level Advanced Certification Programme in Artificial Intelligence and Autonomous Systems aims to prepare professionals to lead and navigate the era of intelligent automation.

The PG Level Advanced Certification Programme in Artificial Intelligence and Autonomous Systems by IISc, in association with TalentSprint, prepares professionals to become adept at managing and scaling intelligent automation initiatives in their organisations.

• IISc edge: Get certified by IISc, India's #1 University and #1 Research Institute*
• High Impact Format: Leverage the distinctive pedagogy of LIVE Online Sessions, use-cases, industry interaction, capstone projects and campus visits.
• Accomplished Faculty: Curriculum taught by an accomplished faculty group that dons research credentials from premier institutions and leading research groups at IISc.
• Mentor Support: Opportunity for direct interaction with industry experts and practitioners
• Experiential learning with iPearl: Attend live sessions on iPearl.ai -an award-winning AI-powered learning platform.

*As per NIRF Ranking 2024

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.

By successfully completing the programme, you will increase your relevancy in projects that require the deployment of intelligent automation while also expanding your career opportunities through developing new-age skills.

• Derive Actionable Insights for your business
  • Master Reinforcement Learning, Neural Networks
  • Automate Critical Decision-Making
  • Amplify your organization’s business impact
• Accelerate your Career Growth
  • Implement an autonomous system with AI functionalities
  • Get certified by the Centre for Continuing Education at IISc
  • Have a holistic industry impact

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

You are eligible if you hold

• A B.E/B.Tech/M.E/M.Tech or an equivalent degree and a minimum of 4 years of professional experience.
• An adequate knowledge of coding and Python and a good exposure to Engineering Mathematics.

The programme will be delivered in an interactive online format, retaining effectiveness. The format uniquely combines the benefits of an in-class programme with the flexibility and safety of online learning.

• Synchronous programme delivery by expert faculty
• Interact and get doubts answered by faculty
• Peer learning through working in groups with other participants
• Support from mentors for projects
• Re-attend classes through recorded archives
• Access videos easily with searchable and indexed video archives
• Learn from the comfort and safety of your home
• Office hours with one-on-one mentor support

Your peer group will include

• Professionals interested in transitioning to AI based roles
• Professionals interested in working on intelligent automation initiatives in their organisations.

This is an expertise-building programme for experienced professionals. You will obtain a certification from the Centre for Continuing Education (CCE) - IISc, the India’s #1 University and #1 Research Institute.

A certificate of successful completion is provided upon completion of all programme requirements. All examinations and evaluations related to the certification are carried out by the Centre for Continuing Education (CCE), IISc.

The programme format with LIVE Interactive Sessions, Use-cases and Capstone projects, Industry Interaction, and Campus immersion enables a conducive environment to network with your peers in the cohort. Most participants can continue to network even after the cohort by using the alumni portal.

As an institution of national importance, IISc was recognised as an Institution of Eminence (IoE) by the Government of India in 2018 and has been consistently ranked among the top Indian institutions in world university rankings. Hence this is a fully approved certification programme offered through IISc.

All participants must execute, before the start of the program, an agreement that consists of the standard programme Terms and Conditions. It has the following components:

Part A: Etiquette and Platform Rights
Classroom Etiquette & General Policy Guidelines
Tools and Platforms in Use
Tools and Platforms: Terms of Use

Part B: Honor Code

Part C: Certification

Part D: Programme Fee, Refund, and Termination Policy

Candidates will be selected based on the details of education, work experience, and a statement of purpose submitted along with the application.

A Relationship Manager will be assigned to the applicant, who will gather all the necessary information and represent his/her candidature to the selection committee for review. The profiles will be sent to IISc for selection.

Join in these easy steps:

• Share your details and apply for the programme
• Submit all the necessary documents
• The programme selection committee will evaluate your candidature
• Upon selection, complete your enrollment and join the programme

Module 0: Bridge Module (Online Module)

1. Basics of Probability
2. Basics of Linear Algebra

Module 1: Introduction to Data Science and Machine Learning (Online Module)

1. Introduction to Machine Learning and Workflow and Applications
2. Linear and Logistic Regression: Optimization view
3. Data modeling with probabilistic methods
4. Perceptron and Hyperplane classifiers
5. Support vector Machines and Kernel Methods

(Lab Module)

1. Focus: Introductory concepts and algorithms in machine learning.
2. Topics: Python, Naive Bayes, Linear Regression, Logistic Regression, Optimization, SVM, Perceptron, Evaluation metrics.
3. Environments/Tools: Jupyter Notebook, Google Colab, sklearn.

Module 2: Introduction to Deep Learning (Online Module)

1. Introduction to Neural Networks and Deep Learning
2. Multilayer perceptrons and training Neural networks
3. Back propagation algorithm
4. Convolutional Neural Networks
5. Recurrent Neural Networks and LSTMs

(Lab Module)

1. Focus: Deeper understanding of neural networks and their applications.
2. Topics: Multi-layer Perceptron, Convolutional Neural Networks, Transfer Learning (AlexNet).
3. Environments/Tools: Jupyter Notebook, Google Colab, TensorFlow, PyTorch, Keras (with GPU acceleration for larger models)

Module 3: Introduction to Sequential Decision Making and Reinforcement Learning (Online Module)

Pre-requisites: Probability and Linear Algebra

1. Introduction and Real world examples
2. Sequential decision making under Uncertainity: Multi-armed Bandits
3. Markov Decision Process, Value and Q-functions, Finite and infinite horizon problems
4. Value and Policy iteration, Dynamic ProgramingMarkov Decision Process, Value and Q-functions, Finite and infinite horizon problems (Instructor: AD)
5. Policy evaluation with Monte-Carlo and Stochastic approximation (Instructor: AD)

(Lab Module)

1. Focus: Principles of reinforcement learning and decision-making.
2. Topics: Multi-armed bandits, Markov Decision Processes, Policy Iteration, Value Iteration, Monte Carlo, OpenAI Gym environments.
3. Environments/Tools: Jupyter Notebook, OpenAI Gym (CartPole, FrozenLake, Blackjack, Grid World)

Module 4: Reinforcement Learning Algorithms (Online Module - 36 Hours)

Pre-requisites: Modules-1,2,3, and 4

1. Data-Driven Optimization
2. Model-free algorithms for policy evaluation – incremental update temporal difference learning, n-step temporal difference learning
3. Model-free least squares temporal difference learning, least squares policy evaluation, incremental least squares temporal difference learning
4. Model-free value-based algorithms for control – SARSA, Q-learning, Expected SARSA, Double Q-learning
5. 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)
6. Off-policy value-based algorithms with approximation – semi-gradient methods, gradient descent in Bellman error, gradient temporal difference learning, temporal difference with correction
7. Policy gradient algorithms – policy parameterization, REINFORCE: Monte-Carlo policy gradient, REINFORCE with baseline, Actor-Critic algorithms
8. Recent algorithms - Deep Q-network, Trust Region Policy Optimization, Proximal Policy Optimization, A2C, A3C

(Lab Module)

1. Focus: Advanced reinforcement learning algorithms.
2. Topics: Monte Carlo Simulation, N-step TD, SARSA, Expected SARSA, SARSA Max, Q-learning, DQN, DDQN.
3. Environments/Tools: OpenAI Gym (CartPole, Super Mario, Pac-Man, Taxi, Mountain Car, Bipedal Walker, Hopper Robot, Four-legged Ant, Lunar Lander, Robot-arm), Acme, Gymnasium

Module 5: RL Applications to Autonomous Systems (Online Module)

Pre-requisites: Modules-1,2,3, and 4

1. Deep RL demonstrations on simulated environments
2. Introduction to Robotics

(Lab Module)

1. Focus: Reinforcement learning algorithms for discrete action spaces.
2. Topics: PPO, DDPG.
3. Environments/Tools: OpenAI Gym (CartPole, Super Mario, Pac-Man, Taxi, Mountain Car), Stable baseline - 3

Capstone Project - Hybrid module – Demo for evaluation

Class start date as January 2025.

It's a 9-month executive programme.

The programme includes

• Live Interactive Sessions by IISc Faculty
• One campus visit at IISc
• Best-in-class Self-learning Content, Use-cases, and Industry Capstone Projects

The programme will be delivered through faculty-led interactive live sessions on TalentSprint’s ipearl.ai. These online sessions will be held at a schedule convenient for working professionals. Participants will be able to interact with the faculty through video, audio, and chat. They will also be able to chat with fellow participants of the programme during online sessions.

As per the programme norms, participants have to maintain at least 75% of attendance during the programme. Going by the curriculum, it is recommended that participants maintain attendance for enriched learning. In the event of unavoidable circumstances, if any, one can access the recorded lectures on our platform to cover up for the missed sessions.

The programme includes continuous formative and summative assessments. The final scores will be determined based on the sum of scores of various assessments.

Yes, participants are required to have their laptops.

Reading material (pre/post session) will be shared regularly through the Online Learning Management System. The access will be available for 9 Months from the date of the commencement of the programme.

A certificate of successful completion is provided upon completion of all requirements of the programme. IISc carries out all examinations and evaluations related to the certification.

For programme fee-related details, please visit Fee page. You can also contact programme RM to know more. Campus visit fee will be based on actuals and to be borne by the participants.

Flexible EMI options are available that enable you to pay the programme fee in instalments. Please reach out to your RM to know more about the schemes available.

Yes. For scholarship-related details, please visit Fee page. You can also reach out to your RM for more details.

No, the application fee is non-refundable.

No. The programme fee is non-refundable and non-transferable under any circumstances.

Professionals have multiple options for funding.

• Sponsored by employer
• Self-funded: Based on our experience of enabling 3000+ professionals who have participated in our executive programs, self-funding is the most preferred option with over 80% choosing this.

Three key reasons why professionals opt for it:

• Freedom: To opt for a programme of their choice, at a time of their choice, with an institution of their choice and not be restrained by the organisation’s policy.
• Flexibility: To pursue greater career opportunities well even beyond their current employer or to nurture their entrepreneurial ambitions.
• Funding: Access to flexible EMI schemes (paying as low as Rs.9,637 per month).

We have also observed that the commitment levels were higher among self-funding participants.

iPearl.ai (Interactive Platform for Experiential and Remote Learning) is created by TalentSprint’s engineering team led by its Chief Technology Officer, an IIT Kanpur alumni. The platform integrates best-in-class open source technologies and SaaS services and perfected them over the last seven years.

When this platform was built, the objective was to offer it to TalentSprint’s partners and customers for digital learning and delivery. Since 2013, the platform has been extensively used by IIM Calcutta, IIT Kanpur, IIIT Hyderabad and IIT Hyderabad, and many more of our customers and partners. More than 300,000 students have used this platform, which is reputed for user experience, quality, and scale.

This feature-rich platform has become so popular that some of the partners are now using it for their academic needs.

The tried and tested direct-to-device platform enables 24 x 7 learning for its users. Some key benefits of the platform are:

• Intuitive student experience through a user-friendly LMS
• Seamless faculty experience through state-of-the-art Studios
• Highly Interactive Online Classes
• Effective group/peer learning/projects through Breakout Rooms
• Proctored Online Assessments with detailed analysis
• Insightful Reports on student engagement and performance
• Flexible integration with other components, as needed

A comparative study of iPearl.ai against Canvas, Impartus, Moodle, Google Classroom, Microsoft Teams, and Zoom was conducted. Features in the following categories were compared:

• Instructor-led Learning
• Self-paced Learning
• Assessments/Evaluations
• User Experience
• Customer Support
• Scalability and Performance
• Integration and Customization
• Others

Do not worry. In case you miss a session, you will be given access to view the recorded version of the session soon.

• An internet-connected device, Computer/Laptop/Tablet/Smartphone, is enough to access the platform.
• Web camera and Mic with necessary power backup will be needed for proctored online assessments.

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 iisc.ac.in

The programme will be delivered by IISc’s Centre for Continuing Education (CCE). CCE delivers courses suitably designed to meet the requirements of target groups: Research & Development (R&D) Laboratories and Industries, Research Scientists/Engineers, to enable them to grow into competent managers of technology intensive and data-driven organisations. For more information, visit http://cce.iisc.ac.in