Applications Closed for Batch 4 | Notify Me for the Next Cohort

  • India’s #1 Research Institute (2021-24) & #1 University (2016-24)
    NIRF
  • 9 Months Executive Friendly
    Programme
  • 180 Hours Immersive
    Learning
  • 2 DaysCampus Visit
    at IISc

Robolution is the future

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

  • " 78% of companies have implemented or planned to
    implement RPA." 2
  • "The global installed robotics capacity is expected to cross 700,000 units by 2026." 3
  • "The autonomous navigation market to reach $13.5 Bn by 2030." 4 - Ministry of Electronics & Information Technology

Notify Me

91-8977631684

Curriculum

With the state-of-the-art curriculum, delivered by IISc faculty members, gain an insight into
AI implementationin the modern context of autonomous systems with the
possibility to apply the learnings to real-world tasks.

Online Module

  • Basics of Probability
  • Basics of Linear Algebra

Online Module

  • Introduction to Machine Learning and Workflow and Applications
  • Linear and Logistic Regression: Optimization view
  • Data modeling with probabilistic methods
  • Perceptron and Hyperplane classifiers
  • Support vector Machines and Kernel Methods

Lab Module

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

Online Module

  • Introduction to Neural Networks and Deep Learning
  • Multilayer perceptrons and training Neural networks
  • Back propagation algorithm
  • Convolutional Neural Networks
  • Recurrent Neural Networks and LSTMs

Lab Module

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

Online Module

Pre-requisites: Probability and Linear Algebra

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

Lab Module

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

Online Module

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

  • Data-Driven Optimization
  • 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

Lab Module

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

Online Module

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

  • Deep RL demonstrations on simulated environments
  • Introduction to Robotics

Lab Module

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

Tools covererd

aias-tools aias-tools

Is this programme for me?

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

Eligibility

  • Qualification: B.Sc/M.Sc or any 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

Enrolment Process

    Apply for the Programme

    Submit Documents

    Selection

    Join the Programme

Notify Me

Admissions closed for Batch 4

What is the return on my investment?

Derive Actionable Insights for your business

  • neural-network Master Reinforcement Learning, Neural Networks
  • automate-decision-making Automate Critical
    Decision-Making
  • business-impactAmplify your organization’s
    business impact

Accelerate your Career Growth

  • ai-functionalities Implement an autonomous system with AI functionalities
  • certificateGet certified by the Centre for Continuing Education at IISc
  • industry-impactHave a holistic
    industry impact

Artificial Intelligence and
Autonomous Systems Course Fee

Application Fee₹2,000


Programme Fee ₹2,80,000 Program Fee with Scholarship ₹2,10,000 (18% GST extra as applicable)


Special Pricing for Corporates*

*Applicable only for enterprises nominating their employees as a group

Fees paid are non-refundable and non-transferable.

Modes of payment available


  • Internet Banking

  • Credit/Debit Card

  • UPI Payments

Easy Financing Options

Interest-Based Schemes

EMI as low as ₹8,897/Month

EMI Options

Loan Partners