M.Tech. (ICT)

Program Overview | Program Outcomes | Semester-wise Program Structure | Course Details | Admission Process

Program Overview

Master of Technology in Information and Communication Technology – M.Tech. (ICT) is a full-time two-year (four semesters) program. The program has been specially designed to meet the increasing needs of professionals who would be able to respond to the convergence between computers and communication systems. The program aims to provide exposure to students who wish to build a professional career in ICT, working at the intersection of technology, research, and development in the areas of Machine Learning and applications to speech, image and vision, natural language processing and others, Data Analytics, Cyber Security, Distributed Computing, Software Engineering,   Signal Processing, Embedded Systems, VLSI Subsystem Design, FPGA, Low-power VLSI Design and Nano electronics.

The Program curriculum includes four specializations tracks that provide a strong foundation and advanced courses in each track. This program tries to leverage the strength and diversity of our faculty and currently offers the following specialization tracks:

  • Machine Learning
  • Signal Processing and Machine Learning
  • Software Systems
  • VLSI and Embedded Systems

Apart from courses, students are required to pursue one full year (two semesters) of research under the guidance of a faculty advisor and submit a master’s thesis in order to obtain the degree of M.Tech. (ICT) specializing in the respective track. On successful completion of the program, the students will be able to acquire essential technical and practical knowledge for solving real-world problems in the ICT domain using modern technologies and tools, and will have the ability to demonstrate excellent analytical and logical problem-solving skills. Apart from receiving rigorous exposure to various areas of scholastic study and research, students are at the same time groomed to cultivate sound professional ethics.

The structure of the curriculum is broadly classified into 4 categories. The first category, referred to as Program Core, is a set of compulsory courses mandatory for every student in the program. Specialization Core courses impart domain knowledge, foundational as well as advanced, in respective specializations, and are offered to students of respective specializations. The third category is Electives, which may or may not be chosen to align with a specific specialization, and allows one to go beyond his/her own specialization. The fourth one is the  Research Thesis, spread over the third and fourth semesters. A student is required to carry out research under the supervision of a faculty member at DA-IICT in an area of mutual interest.

Program Educational Objectives

  • Provide students with a strong foundation of core principles in specialized areas of ICT.
  • Provide students adequate knowledge and hands-on experience in a specialization selected
    by the students.
  • Prepare students to solve and analyze real-world problems using modern tools and research
  • Prepare students for research and development in industry and organization and motivate
    them for higher studies. Prepare students for their contributions in research and development
    by pursuing higher studies in the field of engineering, science, business, or administration.
  • Prepare students with the necessary theoretical background and technical skills to work
    professionally as software engineer, system analyst, research scientist, entrepreneur, software
    developer, and teaching professionals.

Program Outcomes

After successful completion of the MTech program students will have:

  • Essential technical and practical knowledge for solving real-world problems in the field of ICT
  • Ability to demonstrate excellent programming, analytical, logical and problem solving skills that
    would bridge digital divide between urban and rural developments.
  • Ability to use modern engineering tools and technologies necessary for engineering practice in
    industry and R&D organizations.
  • Ability to acquire social and ethical attributes that enable them in applying their skills for societal
  • Ability to communicate effectively both orally and written.

Semester-wise Program Structure



Credit Structure

Semester 1

Program Core 1


Program Core 2


Specialization Core 1


Specialization Core 2


Specialization Core 3


Semester 2

Specialization Core 4


Specialization Core 5


Specialization Core 6




Semester 3

Specialization Core 7


Specialization Core 8




Semester 4



Total Credits



The credit structure of a course is given by a sequence of 4 numbers: (1) Number of lecture hours per week (L), (2) Number of tutorial hours per week (T), (3) Number of lab hours per week (P), and (4) the Total credit of the course (C). 1 lecture hour/week contributes 1 credit; 1 tutorial hour/week contributes 1 credit; 2 laboratory hours/week contribute 1 credit

The curriculum mandates a minimum of 56 credits, 37 earned through coursework and 19 through research credits. Out of the 37 required coursework credits, 5 credits are allocated to compulsory courses (Program core), 29 credits are allocated to Specialization core courses, 3 credits are allocated to an elective.

The distribution of courses for M.Tech. (ICT) degree is as under:

Subject area

No. of credits

Program Core courses


Specialization Core courses


Elective courses


Thesis work


Total credits


Course Details

The following is a representative list of courses. There may be a few minor changes and updates to this list.

Semester 1:

Machine Learning Signal Processing and Machine Leaning Software Systems VLSI and Embedded Systems
Program Core

Programming Lab

Communication Skills and Technical Writing

Specialization Core

Probability and Random Variables Linear Algebra, Random Variables and Random Processes Probability and Random Variables Introduction to Embedded Systems
Linear Algebra and Optimization Advanced Digital Signal Processing Advanced Algorithms

Basics of VLSI

Accelerated Computing Introduction to Machine Learning Advanced Software Engineering Digital Design using HDL and FPGA

Semester 2:

Machine Learning Signal Processing and Machine Leaning Software Systems VLSI and Embedded Systems

Specialization Core

(Any Three)

Advanced Image Processing Detection and Estimation Information Security Digital System Architecture
Pattern Recognition and Machine Learning Adaptive Signal Processing Distributed Systems Embedded System Design
Information Retrieval Topics in Deep Learning Distributed Databases VLSI Subsystem Design
Brain Cognitive Science Wavelet Signal Processing Advanced Computer Networks Analog IC Design
Computational Shape Modeling

Semester 3:

Machine Learning Signal Processing and Machine Leaning Software Systems VLSI and Embedded Systems

Specialization Core 

(Any two)

Computer Vision Adversarial Machine Learning Distributed Systems Embedded System Design
Deep Learning Accelerated Computing Distributed Databases VLSI Subsystem Design
Computer Vision Advanced Computer Networks Analog IC Design


M.Tech Thesis

Semester 4:

Machine Learning Signal Processing and Machine Leaning Software Systems VLSI and Embedded Systems
Thesis M.Tech Thesis (Continuation)

Admission Process

Details on the application process, admission criteria, fee structure and financial assistance can be found here