Master of Technology [M.Tech] (Information Technology) Syllabus, Course Structure and Program Schedule 2025-2026

Course Structure

The course of the program has been designed in a manner that students can excel and specialize according to their interests. The delivery method of the program includes lectures from experienced faculties and guest lectures from experts.

Industrial internship or academic research is a mandatory part of the program, in which every student has to participate in order to complete the M. Tech (IT).

The experience gained during the internship period can prove to be highly useful for the students at the time of recruitment.

Syllabus

The curriculum for M.Tech. IT comprises of foundational course which include advanced programming principles, mathematical background, algorithms, digital signal processing, databases, fundamentals of software engineering and networking.

All these topics build up a base for next semesters, where students begin to specialize in Computer Science, Data Sciences, Networking and Communication, Embedded Systems, Software Engineering.

An overview of the courses and topics covered in M.Tech. IT is given below:

Subject	Topics	Description
Foundation of Computing System	Digital computer fundamentals, Data Structure, Algorithm, DBMS, Software Engineering	This subject covers topics like Logic Circuits, Abstract Data Types, Database type Concepts, Software Requirement Specifications.
Foundation of Communication and Networking	Communication media, Data Link Layer, Network Layer, Application Layer, Wireless-LAN	Detailed study about Coding, Modulation and Formulation with routing table and routing algorithms are covered in these topics.
Advanced Computer Architecture	Fundamentals of Computer Design, Instruction Level Parallelism, ILP with Software Approaches, Memory Hierarchy Design, Multiprocessors And Thread Level Parallelism	Set of Principles, Concepts, Data hazards are studied in this subject.
Advanced Operating System	Architectures of Distributed Systems, Distributed Deadlock Detection, Distributed shared memory, Protection and security, Multiprocessor operating systems	Communication Networks, load distribution algorithm and topics related to operating system design issues are covered in these subjects.
Data Warehousing And Data Mining	Data warehousing: Introduction, Defining the business requirements, Principles of dimensional modeling, Data Mining, Web mining	Dimensional Analysis, Requirements of Data Design, Web mining techniques are the core objectives of this subject.
Advanced DBMS	Overview of Existing DBMS Models, Query Processing basics and optimization, Distributed Databases, Object Oriented Concepts, Enhanced data models for Advanced applications	Introduction to Commercial Database, distributed database are the mains of this subject.
Internet and Web Technology	Web Technology, Web services, Platform for Web Services Development, Web Transactions, Web Service Case Study	Web Technologies, Distributed Computing, Web transactions are taught in this subject.
Pattern Recognition	Introduction, Bayes Decision Theory, Learning, Parametric Discriminant Functions, Error Assessment, Feature Extraction, Margins and Kernel Based Algorithms	The nature of statistical pattern recognition, General framework, Optimal decisions is taught in this subject.
Soft Computing	Neural Network, Unsupervised learning in Neural Network, Fuzzy systems, Genetic algorithm, Evolutionary Computing	Concepts & Applications, Basic Terminologies are taught in this subject.
Simulation and Modeling	Introduction to Simulation and Modeling, System Simulation and Continuous System Simulation, System Dynamics & Probability concepts in Simulation, Simulation of Queueing Systems and Discrete System Simulation, Introduction to Simulation languages and Analysis of Simulation output	What is Natural Language Processing, Ambiguity and uncertainty in language are taught in this subject.