M.Tech in Artificial Intelligence and Data Science

The Objective of this program is to train students with a B.Tech degree in any of Computer Science and Engineering, or Electronics & Communications Engineering, or Electrical and Electronics Engineering, academically and through practice, to specialize in Artificial Intelligence and Data Science and earn a Master’s degree in this domain.

Course Objective

The Objective of this program is to train students with a B.Tech degree in any of Computer Science and Engineering, or Electronics & Communications Engineering, or Electrical and Electronics Engineering, academically and through practice, to specialize in Artificial Intelligence and Data Science and earn a Master’s degree in this domain. Students with a B.Tech in Artificial Intelligence are also welcome if they are interested in earning a higher degree and acquiring greater depth of specialization in this domain.

In accordance with the stated objective, this course provides in the first two semesters courses that build up his (her) understanding of Data Bases, Big Data Analytics and also the approaches and tools of High Performance Computing. It also introduces fundamentals of Natural Language Processing (NLP) and Digital Image Processing and then follows it up with corresponding courses in Computational Sequence Modelling and Deep Learning. NLP and Image Processing and their mappings into Sequence Modelling and Deep Learning form the common basis of this program and acquiring professional competence in Artificial Intelligence. Students are also introduced to Reinforcement Learning with a flavor of Autonomous Systems. Additionally, they are introduced to Financial Risk Management where Data Analytics is going to play a growing role. Further, students are given the option of specializing in any one of the following three streams: Bio-Informatics, Communications, or Smart Industry. Each stream has a basic course followed by an advanced course.

The products of this program, i.e. the graduating students, are expected to be imbued with both motivation and competence to enter the fast-changing professional world of Artificial Intelligence and Data Science, or delve deeper into this domain and advance the state of the art by embarking into a Doctoral program in a related domain.

Total Credits: 62

Prior Degree needed: B.Tech in CSE, AI, ECE or EEE.

  • Core Courses: 33 credits
  • Elective Courses: 9 credits
  • Summer Internship: 2 credits
  • Dissertation: 18 credits

Curriculum Outline


Semester 1
S.No Course Name Credit Remarks
1 Advanced Database Systems 3 Builds upwards from B.Tech level DBMS.
2 High Performance Computing 3 Essentials of HPC along with GPU programming concepts also to be covered.
3 Mathematics for AI and Data Science 3 Covers Linear Algebra and Matrices. Specific elements of Probability and Statistics. Optimization techniques as relevant to AI & ML.
4 Applied Machine Learning 3 Machine Learning with relevant programming aspects.
5 Natural Language Processing 3 Natural Language Processing
Semester 2
S.No Course Name Credit Remarks
1 Big Data Analytics 3 Big Data concepts and platforms with utilization of HPC.
2 Reinforcement Learning 3 From the start of Markov decision process through Q-Learning to multi-agent systems to actor-critic formalisms to Deep QNNs to practical examples.
3 Digital Image Processing 3 Fundamentals, Image enhancements, Image Segmentation, restoration, representations and descriptors.
4 Neural Networks and Deep Learning 3 ANNs, backpropagation and related algorithms. CNNs. Real time computations. GANs. Explainability.
5 Elective Basket One: Any one of these three:
Foundational Bio-informatics
OR
Communication Theory
OR
Control Systems
3 Each of these 3 options represent a Theme or Stream Area. The Electives in the next semester would be in sync with these.
Summer
S.No Course Name Credit Remarks
1 Industry Internship 2 To have an exposure of industry
Semester 3
S.No Course Name Credit Remarks
1 Computational Sequence Modelling 3 Recurrent Neural Networks, LSTMs and GRUs, applications to regression and classification problems in sequences, Word Embeddings and models, Encoder-Decoder frameworks, Neural Machine Translation, BEAM search, Attention Models and Transformers, video activity recognition.
2 Financial Risk Management 3 Foundations of Risk Management, Financial Markets & Products, Valuation & Risk Models, Quantitative Analysis and role of Big Data, Market Risk Management, Credit Risk Management, Risk and Investment Management, Operational and Integrated Risk Management
3 Elective - 2 3 Advance Elective course consistent with Elective Basket One:
1)Computational Genomics OR
2)Deep Learning in Communications OR
3)Industrial Informatics and Algorithmic IoT
4 Elective - 3 3 Free Elective
5 Dissertation Project 2 Problem Statement Identification
Semester 4
S.No Course Name Credit Remarks
1 Dissertation Project 16 Project Demonstration with Publication