Indian Institute of Technology Jodhpur



Course Booklet




Part-Time M. Tech. (AI)





offered by the



IIT Jodhpur with its Technology Partner: NVIDIA



January 2021



Part-Time M. Tech. in Artificial Intelligence (AI)


Artificial Intelligence (AI) is a branch of computer science that aims to create machines to act with higher levels of intelligence and emulate the human capabilities of sense, comprehend and act. The core problems of artificial intelligence include programming computers for certain traits such as Knowledge, Reasoning, Problem-solving, Perception, Learning and Planning. AI technology development and applications are evolving rapidly with major implications for economies and societies. As the demand for such applications increases, there is also an increasing need for building the future workforce for AI. For developing the AI ecosystem, this program will be executed in synergy with other M.Tech programs running in IIT Jodhpur, such as Sensors & IoT, Cyber-Physical Systems, and Advanced Manufacturing and Design.


This Part-Time M. Tech in AI program will offer students with deep knowledge of both fundamental AI technologies, as well as application-oriented AI. A student completing this program will be capable to undertake careers in industry as well as academia. He/She will have the option to explore a variety of domains including Manufacturing, Fintech, Healthcare, Agriculture/Food Processing, Education, Retail/Customer Engagement, Human and Robot interaction/intelligent automation, Smart City, Aid for Differently Abled/Accessibility Technology.

Expected Graduate Attributes:

After completing this programme, a student will be able to develop an ability to:

  1. Comprehend fundamental concepts and hands-on knowledge of the state-of-the-art AI methodologies.
  2. Design and Build real-world AI systems for complex planning, decision making and learning, solving application-specific problems, and to reason about them.
  3. Conceive, Design and Develop Intelligent multi-modal multi-sensory Man-Machine interfaces.
  4. Design, Develop and Deploy machine learning based applications using structured and unstructured data (e.g., speech, text, images/videos).
  5. Understand and Assess reliability, dependability and trust-worthiness of AI-based systems.
  6. Design and develop AI applications for resource constrained environments.
  7. Adhere to evolving ethics and privacy laws across various domains and territories.
  8. Plan and manage technical projects.

Learning Outcome:

  1. Understand the fundamentals of Artificial Intelligence, Machine Learning, Inference Engines, Speech, Vision, Natural Language Understanding, Robotics, and Human Computer Interaction.
  2. Unify the knowledge of human cognition, AI, Machine Learning and data engineering for designing systems.
  3. Demonstrate hands-on knowledge of state-of-the-art AI tools for real-world problem-solving.
  4. Ability to develop real-time and robust AI-based systems with specific software, hardware and data requirements.
  5. Build solutions to explore fully immersive computer-generated worlds (in VR), and overlay computer graphics onto our view of our immediate environment (AR) along with smart, cognitive functionality.
  6. Demonstrate advanced skills to comprehend and communicate effectively.
  7. Carry out projects using intelligent cognitive solutions provided by AI algorithms to get more insights in stakeholder management, risk modeling, intelligent resource scheduling and managing project constraints with intelligent use of data models.





Tentative Course Structure for

Part-Time M. Tech. (AI)


List of compulsory courses

1.      Statistics, Matrix Computation and Optimization (3 credit)

2.      Artificial Intelligence-1 (3 credit)

3.      Data Structure and Practices (1 credit)

4.      Machine Learning-1 (3 credit)

5.      Artificial Intelligence-2 (3 credit)

6.      Machine Learning – 2 (3 credit)

7.      Real Time Autonomous System (2 credit)

8.      Technical Communication (Non-graded) (1 credit)

9.      Ethics and Professional Life (Non-graded) (1 credit)

10.  System Engineering and Project Management (Non-graded) (1 credit)

11.  Intellectual Property (Non-graded) (1 credit)

12.  Major project (16 credit)


List of Elective Courses

                   Please see below to get a list of elective courses which may be offered by IIT Jodhpur. Total 6 program electives (18 credits) and 2 open electives needs to be completed.







Credit Distribution


Program Core

18 credits


Program Electives

18 credits


Open Electives

6 credits



16 credits



4 credits


62 credits



Program Electives for Part-Time M.Tech. (AI)


Courses offered by Department of Computer Science and Engineering

·         Advanced Computer Graphics

·         Algorithms for Big Data

·         AI for Finance

·         Bio-image computing

·         Blockchain

·         Computer Graphics

·         Computer Vision

·         Computational Optimization

·         Computer Architecture

·         Data Visualization

·         Dependable AI

·         Digital Image Analysis

·         Edge and Fog Computing

·         Embedded Systems

·         GPU Programming

·         Graph Theory and Applications

·         Human Machine Interface

·         Information Retrieval and Web Mining

·         Introduction to Augmented Reality and Virtual Reality

·         Machine Learning with Big Data

·         Natural Language Processing

·         Neuromorphic Computing and Design

·         Ad hoc Wireless Networks

·         Selected Topics in Artificial Intelligence - I

·         Selected Topics in Artificial Intelligence - II

·         Selected Topics in Artificial Intelligence - III

·         Selected Topics in Computer Science - I

·         Selected Topics in Computer Science - II

·         Selected Topics in Computer Science - III

·         Social Network Analysis

·         Software and Data Engineering

·         Security and its Applications

·         Speech processing

·         Stream Analytics

·         Vehicular Ad-hoc Networks (VANETs)


Courses offered by Department of Electrical Engineering

·         Adaptive Signal Processing

·         Advanced Control System

·         Advanced Digital Communication

·         Advanced Signal Processing

·         Analog and Interfacing Circuits

·         Antenna Engineering

·         Applied Optimization for Wireless Communication

·         Backhaul Networks for Wireless Systems

·         Coding Theory

·         Compressive Sensing

·         Computational Imaging

·         Cyber Physical System Modelling Laboratory

·         Data Compression

·         Digital image and Video Processing Lab

·         Digital Image Processing and Applications

·         Digital Signal Processing

·         Digital Video Processing

·         Digital VLSI  Design

·         Embedded System Design

·         Embedded System Design Lab

·         Flexible and Printed Electronics

·         Free Space Optical Communications

·         GNSS Signal Processing

·         Image Sensor Design and Applications

·         Introduction to Cyber-Physical Systems

·         Machine Learning for Communication

·         Mathematical Modelling and Simulation

·         Microfluidics Technology

·         Microsystems Fabrication Technology

·         Millimeter Wave Technology

·         Multi-rate Digital Signal Processing

·         Nanosensors

·         Network Information Theory

·         Neuromorphic computing and design

·         Optical Fiber Communications

·         Optimal Filtering

·         Physical Layer Security

·         Principles of Data and System Security

·         Real Time Communications

·         Resource Constrained AI

·         RF IC Design

·         RF IC Design Lab

·         Selected Topics in Communication I

·         Selected Topics in Communication I

·         Selected Topics in Communication II

·         Selected Topics in Communication III

·         Selected Topics in Sensors & IoT I

·         Selected Topics in Sensors & IoT II

·         Selected Topics in Sensors & IoT III

·         Selected Topics in Signal Processing I

·         Selected Topics in Signal Processing II

·         Selected Topics in Signal Processing III

·         Sensors and IoT Lab

·         Sensors and Measurement

·         Smart Grid

·         Speech and Audio Signal Processing

·         Statistical Decision Theory

·         Systems-on-Chips Design

·         VLSI Design Lab

·         Wavelets

·         Wireless Communication

·         Wireless Networks


Courses offered by Department of Mechanical Engineering

·         Robotics


Courses offered by Department of Bioscience and Bioengineering

·         Bioinformatics

·         Computational Biology


Courses offered by Department of Mathematics

·         Financial Engineering

·         Computational finance

·         Computational Game Theory

·         Advanced topics in computational PDE

·         Dynamical Systems

·         Stochastic Processes

·         Representation of Finite Groups



Courses offered by Department of Physics

·         Quantum Computing

·         Quantum Information Processing

·         Quantum Cryptography and Coding



Courses offered by IDRP Digital Humanities

·         Digital Humanities




Data structures and practices




Computer Science and Engineering

L-T-P [C]

0–0–2 [1]

Offered for





Computer Programming





The Instructor will:

1. Explain various data structures and provide details to implement and use them in different algorithms


Learning Outcomes

The students are expected to have the ability to:

1. Write, debug and rectify the programs using different data structures

2. Expertise in transforming coding skills into algorithm design and implementation




Laboratory Experiments

Exercises based on

Abstract Data Types: Arrays, link-list/list, hash tables, dictionaries, structures, stack, queues  (4 labs)

Data Structures: Heap, Sets, Sparse matrix, Binary Search Tree, B-Tree/ B+ Tree, Graph  (4 labs)

Algorithm implementation: Quick or Merge sort, Breadth or Depth first search or Dijkstra’s Shortest Path First algorithm, Dynamic programing (6 labs)



1.  Weiss, M. A. (2007), Data Structures and Algorithm Analysis in C++, Addison-Wesley.

2.  Lipschutz, S. (2017), Data Structures with C, McGraw Hill Education.

3.  Cormen, T. H., Leiserson, C. E., Rivest, R. L. and Stein, C., (2009), Introduction to

Algorithms, MIT Press.


Online Course Material

1.       Department of Computer Science and Engineering, IIT Delhi





Artificial Intelligence - I




Computer Science and Engineering

L-T-P [C]

3–0–0 [3]

Offered for

M.Tech. 1st Year, Ph.D. 1st Year









The Instructor will:

1. Cover various paradigms that come under the broad umbrella of AI.


Learning Outcomes

The students are expected to have the ability to:

1. Develop an understanding of where and how AI can be used.



Introduction: Uninformed search strategies, Greedy best-first search, And-Or search, Uniform cost search, A* search, Memory-bounded heuristic search, Local and evolutionary searches (9 Lectures)

Constraint Satisfaction Problems: Backtracking search for CSPs, Local search for CSPs (3 Lectures)

Adversarial Search: Optimal Decision in Games, The minimax algorithm, Alpha-Beta pruning, Expectimax search (4 Lectures)

Knowledge and Reasoning: Propositional Logic, Reasoning Patterns in propositional logic; First order logic: syntax, semantics, Inference in First order logic, unification and lifting, backward chaining, resolution (9 Lectures)

Representation: Information extraction, representation techniques, foundations of Ontology (3 Lectures)

Planning:  Situation Calculus, Deductive planning, STRIPES, sub-goal, Partial order planner (4 Lectures)

Bayesian Network, Causality, and Uncertain Reasoning: Probabilistic models, directed and undirected models, inferencing, causality, Introduction to Probabilistic reasoning (6 lectures)

Introduction to RL: MDP, Policy, Q-value (4 Lectures)



1.        Russel,S., and Norvig,P., (2015), Artificial Intelligence: A Modern Approach, 3rd Edition, Prentice Hall


Self Learning Material

1.       Department of Computer Science, University of California, Berkeley,

2.       NPTEL: Artificial Intelligence,





Artificial Intelligence - II




Computer Science and Engineering

L-T-P [C]

3–0–0 [3]

Offered for

M.Tech. 1st Year, Ph.D. 1st Year









1.       To cover modern paradigms of AI that go beyond traditional learning


Learning Outcomes

The students are expected to have the ability to:

1.       Develop an understanding of modern concepts in AI and where they can be used

2.       Design, implement and apply novel AI techniques based on emerging real-world requirements




Making decisions: Utility theory, utility functions, decision networks, sequential decision problems, Partially Observable MDPs, Game Theory (14 Lectures)

Reinforcement Learning: Passive RL, Active RL, Generalization in RL, Policy Search, (7 Lectures)

Probabilistic Reasoning over time: Hidden Markov Models, Kalman Filters (7 Lectures)

Knowledge Representation: Ontological engineering, Situation Calculus, semantic networks, description logic (6 Lectures)

Planning: Planning with state space search, Partial-Order Planning, Planning Graphs, Planning with Propositional Logic, hierarchical task network planning, non-deterministic domains, conditional planning, continuous planning, multi-agent planning (8 Lectures)


Text Book

  1. S. RUSSEL, P. NORVIG (2009), Artificial Intelligence: A Modern Approach, Pearson, 3rd Edition.


Reference Book

  1. E. RICH, K. KNIGHT, S. B. NAIR (2017), Artificial Intelligence, McGraw Hill Education, 3rd Edition.
  2. R.S. SUTTON, A.G. BARTO (2015), Reinforcement Learning: An Introduction, The MIT Press, 2nd Edition.




Machine Learning  - I




Computer Science and Engineering

L-T-P [C]

3–0–0 [3]

Offered for

M.Tech. (CSE, AI, DCS)




Introduction to Computer Sc., Probability, Statistics and Stochastic Processes


IML, Applied ML, PRML



  1. To understand various key paradigms for machine learning approaches
  2. To familiarize with the mathematical and statistical techniques used in machine learning.
  3. To understand and differentiate among various machine learning techniques.


Learning Outcomes

The students are expected to have the ability to:

  1. To formulate a machine learning problem
  2. Select an appropriate pattern analysis tool for analyzing data in a given feature space.
  3. Apply pattern recognition and machine learning techniques such as classification and feature selection to practical applications and detect patterns in the data.



Introduction: Definitions, Datasets for Machine Learning, Different Paradigms of Machine Learning, Data Normalization, Hypothesis Evaluation, VC-Dimensions and Distribution, Bias-Variance Tradeoff, Regression (Linear) (7 Lectures)

Bayes Decision Theory: Bayes decision rule, Minimum error rate classification, Normal density and discriminant functions (5 Lectures)

Parameter Estimation: Maximum Likelihood and Bayesian Parameter Estimation (3 Lectures)

Discriminative Methods: Distance-based methods, Linear Discriminant Functions, Decision Tree, Random Decision Forest and Boosting (4 Lectures)

Feature Selection and Dimensionality Reduction: PCA, LDA, ICA, SFFS, SBFS (4 Lectures)

Artificial Neural Networks: MLP, Backprop, and RBF-Net (4 Lectures)

Foundations of Deep Learning: DNN, CNN, Autoencoders (5 lectures)

Kernel Machines: Kernel Tricks, SVMs (primal and dual forms), K-SVR, K-PCA (6 Lectures)

Clustering: k-means clustering, Gaussian Mixture Modeling, EM-algorithm (4 Lectures)


Text Book

  1. Shalev-Shwartz,S., Ben-David,S., (2014), Understanding Machine Learning: From Theory to Algorithms, Cambridge University Press

2.       R. O. Duda, P. E. Hart, D. G. Stork (2000), Pattern Classification, Wiley-Blackwell, 2nd Edition.


Reference Book

  1. Mitchell Tom (1997). Machine Learning, Tata McGraw-Hill
  2. C. M. BISHOP (2006), Pattern Recognition and Machine Learning, Springer-Verlag New York, 1st Edition.


Self-Learning Material

  1. Department of Computer Science, Stanford University,




Machine Learning  - II




Computer Science and Engineering

L-T-P [C]

3–0–0 [3]

Offered for

M.Tech. (AI, DCS)






Deep Learning



  1. Provide technical details about various recent algorithms and software platforms related to Machine Learning with specific focus on Deep Learning.


Learning Outcomes

The students are expected to have the ability to:

  1. Design and program efficient algorithms related to recent machine learning techniques, train models, conduct experiments, and develop real-world ML-based applications and products




Fractal 1: Foundations of Deep Learning

Deep Networks: CNN, RNN, LSTM, Attention layers, Applications (8 lectures)

Techniques to improve deep networks: DNN Optimization, Regularization, AutoML (6 lectures)


Fractal 2: Representation Learning

Representation Learning: Unsupervised pre-training, transfer learning, and domain adaptation, distributed representation, discovering underlying causes (8 lectures)

Auto-DL: Neural architecture search,  network compression, graph neural networks (6 lectures)


Fractal 3: Generative Models

Probabilistic Generative Models: DBN, RBM (3 lectures)

Deep Generative Models: Encoder-Decoder, Variational Autoencoder, Generative Adversarial Network (GAN), Deep Convolutional GAN, Variants and Applications of GANs (11 lectures)


Text Book

  1. Goodfellow,I., Bengio.,Y., and Courville,A., (2016), Deep Learning, The MIT Press .


Reference Book

  1.  Charniak, E. (2019), Introduction to deep learning, The MIT Press.
  2. Research literature.


Self Learning Material





Real Time Autonomous Systems




Computer Science and Engineering

L-T-P [C]

2–0–0 [2]

Offered for





Machine Learning I, Artificial Intelligence I






The Instructor will:

1. Provide an understanding about autonomous/ semi autonomous systems like autonomous cars and drones.



Learning Outcomes

The students are expected to have the ability to:


  1. Understand and use the methodologies to design, model and implementation of autonomous systems for real time applications.




Introduction to Agents, Agent Architectures: Subsumption Architecture, Situated Automata, Hybrid Architecture (4)


Real time System Implementation (3)


Mobile agents – locomotion (wheeled, legged, aerial), sensors and mechanisms. (3)


Robot localisation & SLAM (4)


Planning and Navigation (6)


Case Study: Autonomous car – learning to drive; human centered autonomous vehicle. (8)



Laboratory Experiments



1. Seigwart, R. and Nourbakhsh, I.R. Introduction to Autonomous Mobile Robots, 2nd edition, MIT Press 2011

2. Giorgio C. Buttazzo, Hard Real-Time Computing Systems: Predictable Scheduling Algorithms and Applications,” Springer US, Year: 2011

3. Stuart J. Russell and Peter Norwig, Artificial Intelligence: A Modern Approach, 3rd edition, Pearson Press

4. Markus Maurer et al. eds. Autonomous Driving, Springer Link (open access)

5. Gerhard Weiss ed., Multiagent System, Second Edition, MIT Press, 2013


Reference Books

1. Tzafestas, S. G. (Ed.). (2012). Advances in intelligent autonomous systems (Vol. 18). Springer Science & Business Media.

2. Ge, S. S. (2006). Autonomous mobile robots: sensing, control, decision making and applications. CRC press.

1.      3. Mhamed Itmi, Alain Cardon(2016),  New Autonomous Systems,  Wiley-ISTE.

2.      4. De Gyurky, S. M., & Tarbell, M. A. (2013). The Autonomous System: A Foundational Synthesis of the Sciences of the Mind. John Wiley & Sons.





Course Contents for the new Elective courses