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Open 2026-27
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M.Tech. Computer Science and Engineering

program-details

In this program, the students will get the specialized knowledge of hot topics of CSE like Artificial Intelligence, Machine Learning, Data Analytics, Deep Learning, Cyber Security, Internet of Things, Sensors, etc in an effective manner.

Industry Immersion

To undertake industry careers involving innovation and problem solving using software and other information technologies. To undertake research careers in Computer Sciences and allied areas. To contribute to society by becoming a model professional who can communicate effectively and observes ethical behavior.

eligibility criteria

B.E/B.Tech (CSE/IT/ECE) or MCA or M.Sc. (IT/ Computer Science) from a recognized university with atleast 50% marks

Admission criteria

Merit in CT-SET, subject to fulfilling eligibility criteria.

Duration

2 Years

related program

Curriculum

1ST SEMESTER SUBJECTS

This course covers advanced abstract data types and algorithm design paradigms, including balanced trees, heaps, hashing, graph algorithms, and amortized analysis. It emphasizes algorithmic efficiency, complexity analysis, and techniques such as divide and conquer, dynamic programming, greedy methods, and NP-completeness, preparing students to design efficient solutions for complex computational problems.

This laboratory course provides hands-on implementation of advanced data structures and algorithms studied in the corresponding theory course. Students implement and analyze trees, graphs, hashing schemes, and various algorithm design strategies to reinforce theoretical concepts through practical coding exercises.

This course introduces the fundamentals of research, including formulation of research problems, literature review, research design, and data collection and analysis techniques. It equips students with the skills needed to plan, conduct, and report scientific and technical research in a systematic and ethical manner.

This course covers the fundamentals of digital image representation, acquisition, and processing, including image enhancement, restoration, segmentation, compression, and morphological operations. It emphasizes both spatial and frequency domain techniques along with their applications in real-world image analysis systems.

This laboratory course provides practical exposure to digital image processing techniques through implementation of image enhancement, filtering, segmentation, and transformation algorithms using standard tools and programming environments.

This course explores advanced concepts in operating systems including distributed systems, process synchronization, deadlock handling, memory management, file systems, and virtualization. It focuses on the design principles behind modern, scalable, and fault-tolerant operating systems.

This laboratory course offers practical experience with operating system concepts such as process scheduling, synchronization, memory management, and system-level programming, reinforcing the theoretical foundations of advanced operating systems.

This course focuses on the principles and practices of academic writing, structuring research papers, citation methods, and publication processes. It also addresses research integrity, plagiarism, authorship ethics, and responsible conduct in scholarly publishing.

This elective course covers the architecture, deployment models, and service models of cloud computing, including virtualization, storage, networking, and security in cloud environments. It introduces major cloud platforms and their infrastructure services for building scalable applications.

This laboratory course provides hands-on experience in provisioning and managing cloud infrastructure and services, including virtual machines, storage, and networking components on popular cloud platforms.

This elective course introduces the fundamentals of data science, including data collection, cleaning, exploratory analysis, statistical modeling, and visualization techniques. It emphasizes practical approaches to extracting meaningful insights from structured and unstructured data.

This laboratory course offers practical training in data analysis and visualization using data science tools and libraries, allowing students to apply statistical and analytical techniques to real datasets.

This elective course covers the principles and practices of DevOps, including continuous integration, continuous delivery, automated testing, containerization, and infrastructure as code. It focuses on building efficient pipelines for software development and deployment.

This laboratory course provides hands-on experience with DevOps tools and practices, including setting up CI/CD pipelines, containerization, and automated deployment workflows.

2ND SEMESTER SUBJECTS

This course covers advanced topics in computer architecture including pipelining, instruction-level parallelism, memory hierarchy design, multiprocessor systems, and parallel computing architectures. It emphasizes performance evaluation and design trade-offs in modern high-performance computing systems.

This course explores advanced database concepts such as distributed databases, transaction management, and query optimization, along with data mining techniques including classification, clustering, and association rule mining for extracting knowledge from large datasets.

This laboratory course provides practical experience with advanced database management systems and data mining tools, allowing students to implement and evaluate database operations and mining algorithms on real datasets.

This course covers advanced software development methodologies, including agile practices, software architecture, design patterns, quality assurance, and project management. It emphasizes building scalable, maintainable, and high-quality software systems.

This course covers advanced machine learning concepts including ensemble methods, neural networks, deep learning architectures, and reinforcement learning. It emphasizes model evaluation, optimization, and application of machine learning techniques to complex real-world problems.

This laboratory course provides hands-on implementation of advanced machine learning algorithms and models, enabling students to apply learning techniques to practical datasets and evaluate model performance.

This course covers advanced concepts in network security including cryptographic protocols, intrusion detection, firewalls, secure network architectures, and threat mitigation strategies. It focuses on securing modern networked and distributed systems against evolving cyber threats.

This course requires students to identify, design, and implement a substantial project addressing a real-world or research problem in computer science. It emphasizes independent work, application of learned concepts, and documentation of results through a formal project report.

This elective course covers end-to-end web application development, including front-end frameworks, back-end technologies, database integration, and API design. It emphasizes building complete, functional web applications using modern development stacks.

This laboratory course provides hands-on practice in developing full stack web applications, integrating front-end interfaces with back-end services and databases.

This elective course introduces the principles of generative artificial intelligence, including generative adversarial networks, variational autoencoders, and large language models. It explores applications of generative models in text, image, and content generation.

This laboratory course provides practical experience in building and experimenting with generative AI models, including training and fine-tuning models for text and image generation tasks.

This elective course covers the fundamentals of the Internet of Things, including sensor networks, embedded systems, communication protocols, and IoT architectures. It emphasizes the design and deployment of connected devices and smart systems.

This laboratory course provides hands-on experience with IoT hardware and platforms, enabling students to design, program, and deploy connected sensor-based systems and applications.

3RD SEMESTER SUBJECTS

Deep Learning covers the theory and design of artificial neural networks, including feedforward networks, convolutional and recurrent architectures, optimization and regularization techniques, and applications in image recognition, natural language processing, and sequence modeling.

This lab provides hands-on experience in building, training, and evaluating deep neural networks using frameworks such as TensorFlow and PyTorch, covering CNNs, RNNs, and transfer learning for real-world image and text datasets.

Compiler Design covers the principles and phases of compilation, including lexical analysis, syntax and semantic analysis, intermediate code generation, code optimization, and code generation, with emphasis on constructing efficient and correct compilers.

This course covers advanced concepts in data warehouse architecture, OLAP techniques, and data mining methods including association rule mining, classification, clustering, and predictive analytics for extracting actionable knowledge from large-scale data repositories.

Current Affairs develops awareness and analytical understanding of contemporary national and international events, policies, and developments across economic, political, social, scientific, and technological domains relevant to professional and civic life.

Dissertation-I involves identification of a research problem, comprehensive literature review, formulation of research objectives and methodology, and preparation of a preliminary work plan under faculty guidance, culminating in a proposal report.

Blockchain Technology covers the fundamentals of distributed ledger systems, consensus mechanisms, cryptographic foundations, smart contracts, and blockchain platforms, with emphasis on applications in secure, decentralized, and tamper-resistant systems.

This lab offers practical experience in developing and deploying smart contracts, setting up blockchain networks, implementing consensus algorithms, and building decentralized applications using platforms such as Ethereum and Hyperledger.

Software Testing & Quality Assurance covers testing methodologies, test case design, automation frameworks, quality assurance standards, and software reliability metrics, with emphasis on ensuring robust, defect-free, and high-quality software products.

This lab provides hands-on practice in designing test cases, performing manual and automated testing, using testing tools and frameworks, and applying quality assurance techniques across the software development lifecycle.

4TH SEMESTER SUBJECTS

Dissertation-II involves the implementation, analysis, and evaluation of the research work proposed in Dissertation-I, culminating in a complete dissertation report, manuscript preparation, and final viva-voce presentation under faculty guidance.

fees

Details

Amount

Programme Fees (per Semester)

₹60000

Examination Fees

₹3000

International Fees (per Year)

$5300

Fee Slab

Slab >=60% - 74.99% >=75% - 89.99% >=90% & Above
Fee ₹55000 ₹50000 ₹45000

Students can avail these slots depending on the marks they have scored. Each slot reflects a different academic range, helping students understand where they stand and what benefits they qualify for.

Programme Outcomes

Exhibit analytical, decision making and problem solving skills by applying research principles for handling real life problems with realistic constraints. Ability to communicate the findings or express innovative ideas in an effective manner with an awareness of professional, social and ethical responsibilities. To prepare professionals who will have successful career in industries, academia, research and entrepreneurial endeavors. 

Programme Specific Outcomes

Ability to formulate problems, propose algorithm and model efficient scalable systems. Ability to identify learning processes to become independent reflective learners. Ability to explore research gaps, analyze and carry out research in the specialized/emerging areas.

Salient Features

A graduate of the Computer Science and Engineering Program will demonstrate:
Apply the knowledge of engineering principles to develop software systems, products and processes thus to solve real world multifaceted problems. Ability to design and conduct experiments, procedures and technical skills necessary for engineering exploration to solve societal problems and environmental contexts for sustainable development.
To understand contemporary issues in providing technology solutions for sustainable development considering impact on economic, social, political, and global issues and thereby contribute to the welfare of the society. To generate optimized solutions by formulating and implementing analytical tools for upcoming issues in the field of computer science and engineering. 

Infrastructure

Laboratory

Laboratory

What better way to learn working of anything than to get exposed to it first hand!. The specialized laboratories are equipped to conduct advanced practical and research work.