M.Tech Computer Science Engineering (CSE)

Fee Type	Amount
Programme Fees (per Semester)	₹60000
Examination Fees	₹3000
International Fee (USD)	$5300

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

Course 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.

Course Outcome:

CO1: Choose appropriate data structures and algorithms and use it to design solution for a specific problem.
CO2: Execute the operations of hashing to retrieve data from data structure.
CO3: Design and analyze programming problem statements.
CO4: Come up with analysis of efficiency and proofs of correctness.
CO5: Comprehend and select algorithm design approaches in a problem specific manner.
CO6: Understand the principles and types of randomized algorithms.

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.

Course Outcome:

CO1: Identify the basic concept of data structure and identify the need for list data structures and its operations
CO2: Exemplify the concept of stacks and queues with suitable applications.
CO3: Classify the types of tree data structures and explain its functionalities.
CO4: Outline the concept of graph data structures with examples.
CO5: Design the algorithms for searching and sorting techniques.
CO6: Develop problem-solving skills through coding and debugging complex programs using advanced algorithmic techniques.

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.

Course Outcome:

CO1: Understand research methodology concepts and processes.
CO2: Apply research design and sampling techniques.
CO3: Analyze data collection methods and analytical approaches.
CO4: Apply statistical methods in research studies.
CO5: Develop technical writing and research reporting skills.
CO6: Evaluate research ethics and professional research practices.

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.

Course Outcome:

CO1: Have an appreciation of the fundamentals of Digital Image Processing including the topics of filtering, transforms and morphology, and image analysis and compression
CO2: Be able to implement basic image processing algorithms in MATLAB.
CO3: Have the skill base necessary to further explore advanced topics of Digital Image Processing.
CO4: Be able to make a positive professional contribution in the field of Digital Image Processing
CO5: At the end of the course the student should have a clear impression of the breadth and practical scope of Digital Image Processing and have arrived at a level of understanding that is the foundation for most of the work currently underway in this field.
CO6: At the end of the course the student should have a clear impression of the breadth and practical scope of Digital Image Processing and have arrived at a level of understanding that is the foundation for most of the work currently underway in this field.

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.

Course Outcome:

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.

Course Outcome:

CO1: Understand advanced operating system concepts and architecture.
CO2: Analyze process management and synchronization techniques.
CO3: Evaluate memory management techniques.
CO4: Understand file systems and I/O management.
CO5: Analyze distributed and real-time operating systems.
CO6: Apply security and virtualization concepts in modern 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.

Course Outcome:

CO1: Implement CPU scheduling algorithms and evaluate their performance.
CO2: Apply process creation and file management techniques.
CO3: Solve synchronization problems using suitable mechanisms.
CO4: Analyze and resolve deadlock-related issues.
CO5: Implement memory and disk scheduling algorithms.
CO6: Develop shell scripts and multithreaded applications.and integrate citations in research writing

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.

Course Outcome:

CO1: Understand the philosophy of ethics and its application to academic integrity and research conduct.
CO2: Formulate a research problem and research proposal, and apply ICT tools and referencing practices in academic writing.
CO3: Apply publication-ethics guidelines (COPE, WAME) and use citation databases and plagiarism-detection software.
CO4: Identify types of publication misconduct, predatory publishing practices and conflicts of interest.
CO5: Structure, draft and revise a research paper, and navigate the journal submission and peer-review process.
CO6: Use research metrics and open-access publishing models to disseminate research responsibly.

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.

Course Outcome:

CO1: Understand cloud platforms and cloud service environments.
CO2: Configure and manage virtual machines and cloud storage services.
CO3: Implement cloud networking and connectivity solutions.
CO4: Apply cloud security mechanisms and access management.
CO5: Monitor and manage cloud services efficiently.
CO6: Deploy scalable cloud-based applications using modern tools.

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.

Course Outcome:

CO1: Understand cloud computing concepts and service models.
CO2: Analyze cloud infrastructure and virtualization technologies.
CO3: Evaluate cloud storage and resource management techniques.
CO4: Apply cloud networking and security mechanisms.
CO5: Manage cloud services and governance.
CO6: Assess emerging trends in cloud computing.

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.

Course Outcome:

CO1: Understand data science concepts and workflows.
CO2: Apply data preprocessing and exploratory analysis techniques.
CO3: Use statistical methods for data analysis.
CO4: Apply machine learning algorithms for predictive tasks.
CO5: Evaluate predictive models and create visual insights.
CO6: Analyze big data technologies and applications.

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.

Course Outcome:

CO1: Apply Python tools and libraries for data science tasks.
CO2: Perform data preprocessing and exploratory analysis.
CO3: Apply statistical analysis methods on datasets.
CO4: Implement machine learning algorithms for predictive modeling.
CO5: Visualize data and evaluate model performance.
CO6: Apply big data tools and solve real-world analytics problems

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.

Course Outcome:

CO1: Understand DevOps principles and lifecycle.
CO2: Apply source code management and CI tools.
CO3: Implement continuous delivery and automation practices.
CO4: Deploy applications using containers and orchestration tools.
CO5: Apply monitoring and DevSecOps practices.
CO6: Analyze cloud DevOps and emerging industry trends.

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

Course Outcome:

CO1: Understand DevOps tools and workflow environment.
CO2: Apply version control and build automation tools.
CO3: Implement CI pipelines and configuration management.
CO4: Deploy applications using Docker and Kubernetes.
CO5: Apply monitoring and security practices in DevOps.
CO6: Build complete end-to-end DevOps pipelines.

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.

Course Outcome:

CO1: Understand the fundamental principles, models, and methodologies of software development.
CO2: Apply appropriate software engineering models to solve real-world problems.
CO3: Design structured and modular software solutions using appropriate tools and techniques.
CO4: Analyze software quality, reliability, and performance metrics.
CO5: Collaborate effectively in a team environment and demonstrate professional ethics.
CO6: Demonstrate research aptitude and continuous learning by exploring current software engineering trends and tools.

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.

Course Outcome:

CO1: Understand advanced database concepts and database design techniques.
CO2: Apply transaction management and query optimization techniques.
CO3: Analyze data warehousing and OLAP operations.
CO4: Apply data preprocessing and association rule mining techniques.
CO5: Implement classification and clustering algorithms.
CO6: Evaluate advanced data mining applications in real-world scenarios.

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.

Course Outcome:

CO1: Apply advanced SQL concepts and database operations.
CO2: Implement transaction management and query optimization techniques.
CO3: Perform data warehouse and OLAP operations.
CO4: Apply data preprocessing and association mining techniques.
CO5: Implement classification and clustering algorithms.
CO6: Use data mining tools for real-world analytics problems.

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.

Course Outcome:

CO1: Understand advanced software engineering concepts and process models.
CO2: Apply requirements engineering and project planning techniques.
CO3: Design software architectures using modern design principles.
CO4: Apply software testing and quality assurance techniques.
CO5: Analyze software maintenance and configuration management.
CO6: Evaluate modern software engineering trends and practices

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.

Course Outcome:

CO1: Understand machine learning concepts and paradigms.
CO2: Apply supervised learning algorithms.
CO3: Implement unsupervised learning and clustering techniques.
CO4: Analyze neural networks and deep learning models.
CO5: Apply reinforcement learning techniques.
CO6: Evaluate machine learning applications in real-world scenarios.

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.

Course Outcome:

CO1: Apply Python libraries and tools for machine learning tasks.
CO2: Implement supervised learning algorithms for prediction and classification.
CO3: Apply unsupervised learning and clustering techniques.
CO4: Implement neural networks and deep learning models.
CO5: Apply reinforcement learning techniques.
CO6: Develop machine learning solutions for real-world applications.

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.

Course Outcome:

CO1: Understand fundamental principles of network security and security threats.
CO2: Apply cryptographic and authentication techniques for secure communication.
CO3: Analyze various network attacks and defense mechanisms.
CO4: Evaluate firewall architectures and intrusion detection systems.
CO5: Design secure communication using VPN and security protocols.
CO6: Assess wireless, cloud, and emerging security 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.

Course Outcome:

CO1: Identify and formulate a real-world or research-oriented problem in Computer Science.
CO2: Design and develop an appropriate computational solution using relevant concepts and technologies.
CO3: Implement, test, and evaluate the proposed solution using appropriate performance measures.
CO4: Analyse project results and critically assess the effectiveness and limitations of the developed solution.
CO5: Apply independent research and problem-solving skills to address technical challenges.
CO6: Prepare and present a comprehensive technical report documenting the project methodology, results, and findings.

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.

Course Outcome:

CO1: Understand and apply HTML and CSS for designing web pages and layouts.
CO2: Develop responsive and dynamic web pages using CSS, Bootstrap, and PHP.
CO3: Integrate JavaScript for dynamic interactions and client-side scripting.
CO4: Design, create, and manage relational databases using MySQL and perform database operations efficiently.
CO5: Develop full-stack web applications by integrating front-end technologies, PHP, and MySQL to solve real-world problems..
CO6: Deploy, test, and maintain secure, database-driven web applications by applying best practices in validation, error handling, security, and performance optimization.

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

Course Outcome:

CO1: Understand and apply HTML and CSS for designing web pages and layouts.
CO2: Develop responsive and dynamic web pages using CSS, Bootstrap, and PHP.
CO3: Integrate JavaScript for dynamic interactions and client-side scripting.
CO4: Design, create, and manage relational databases using MySQL and perform database operations efficiently.
CO5: Develop full-stack web applications by integrating front-end technologies, PHP, and MySQL to solve real-world problems.
CO6: Deploy, test, and maintain secure, database-driven web applications by applying best practices

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

Course Outcome:

CO1: Explain the fundamental concepts, architectures, and applications of Generative AI and foundation models.
CO2: Apply prompt engineering techniques to generate effective text, images, code, and other AI-generated content.
CO3: Utilize large language models (LLMs), transformer-based models, and Generative AI platforms to develop AI-powered solutions.
CO4: Design AI applications using Retrieval-Augmented Generation (RAG), LangChain, and AI agents.
CO5: Analyze the limitations, ethical issues, and responsible AI practices associated with Generative AI systems.
CO6: Develop and evaluate Generative AI applications for real-world problems across diverse domains using appropriate tools and frameworks.

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.

Course Outcome:

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.

Course Outcome:

CO1: Understand IoT architecture, applications, and implementation challenges
CO2: Identify and interface sensors and actuators with microcontrollers
CO3: Write Arduino code for basic I/O operations
CO4: Apply communication protocols like Bluetooth and MQTT
CO5: Utilize cloud services like ThingSpeak and Blynk for IoT data management
CO6: Design and simulate basic IoT applications and mini projects

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.

Course Outcome:

CO1: Understand and simulate basic IoT circuits using Tinkercad
CO2: Interface sensors and actuators with Arduino and write embedded code
CO3: Analyze and use basic communication protocols like Serial and Digital I/O
CO4: Develop, simulate, and test simple IoT mini projects on embedded platforms
CO5: Install and configure basic development environments for Arduino and Raspberry Pi
CO6: Demonstrate the ability to integrate multiple IoT components into a cohesive working

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 modelling.

Course Outcome:

CO1: Explain the fundamental concepts, mathematical foundations, and architectures of deep learning models.
CO2: Design, train, and optimize deep neural networks using appropriate optimization algorithms and regularization techniques.
CO3: Develop convolutional neural network models for image classification and computer vision applications.
CO4: Apply recurrent neural networks, LSTM, GRU, and attention mechanisms for sequential data and natural language processing tasks.
CO5: Analyze and implement advanced deep learning architectures such as Autoencoders, GANs, Transformers, and Vision Transformers.
CO6: Evaluate deep learning solutions for real-world applications while considering ethical issues, model efficiency, and emerging trends.

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.

Course Outcome:

At the end of the course, the students will be able to:
CO1: Gain practical experience in setting up and utilizing deep learning frameworks such as TensorFlow and PyTorch for various neural network implementations.
CO2: Implement and train neural networks, including perceptron’s, multi-layer networks, CNNs, and RNNs, understanding forward and backward propagation and optimization techniques.
CO3: Develop and apply advanced deep learning models such as GANs, autoencoders, and LSTMs for practical applications including image generation, anomaly detection, and time-series forecasting.
CO4: Explore and implement advanced concepts like reinforcement learning and attention mechanisms, including building and training models for specific tasks.
CO5: Deploy trained deep learning models on cloud platforms, creating APIs for real-world applications, and understanding the practical aspects of model evaluation and deployment.
CO6: Design, evaluate, and optimize deep learning solutions for real-world applications by integrating appropriate architectures, performance metrics, and deployment strategies.

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.

Course Outcome:

CO1: Explain the phases of compilation, lexical analysis techniques, compiler construction tools, and the role of finite automata in lexical analysis.
CO2: Analyze context-free grammars and design efficient parsing techniques using LL, LR, SLR, CLR, and LALR parsing methods.
CO3: Apply semantic analysis techniques, manage symbol tables, perform type checking, and generate intermediate code representations for source programs.
CO4: Evaluate and implement machine-independent code optimization techniques to improve the efficiency and performance of compiled programs.
CO5: Design code generation strategies, register allocation methods, and runtime storage management techniques for target machine architectures.
CO6: Analyze advanced compiler technologies, including JIT compilation, LLVM, parallelizing compilers, domain-specific languages, and compiler support for modern computing systems.

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.

Course Outcome:

CO1: Demonstrate an understanding of the importance of data mining and the principles of business intelligence
CO2: Understand KDD process for finding interesting pattern from warehouse.
CO3: Demonstrate the classification, clustering etc. .in large datasets
CO4: Ability to apply mining techniques for real static data
CO5: Develop a data mining application for data analysis using various tools.
CO6: Perform exploratory analysis of the data to be used for mining.

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.

Course Outcome:

CO1: Identify and describe significant national and international current events.
CO2: Explain the social, political, scientific, and technological dimensions of contemporary issues.
CO3: Analyse the causes, impacts, and implications of major current events.
CO4: Critically evaluate information from reliable news and media sources.
CO5: Communicate informed views on contemporary issues through discussions, presentations, and debates.
CO6: Develop awareness of current affairs relevant to society, professional life, and responsible citizenship.

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.

Course Outcome:

CO1: Identify and formulate a relevant research problem in the chosen area of specialization.
CO2: Conduct and critically analyse the literature related to the identified research problem.
CO3: Define appropriate research objectives, questions, and scope of the proposed research work.
CO4: Develop a suitable research methodology and preliminary work plan for addressing the research problem.
CO5: Prepare and present a well-structured research proposal based on the identified problem, literature review, objectives, and methodology.

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.

Course Outcome:

CO1: Analyze and apply software quality models and assurance standards in complex systems.
CO2: Design and implement robust testing strategies using black-box and white-box techniques.
CO3: Utilize testing tools and automation frameworks in CI/CD environments.
CO4: Evaluate SQA practices, configuration management, and defect prevention strategies.
CO5: Apply testing strategies to cloud, mobile, and AI-based applications.
CO6: Conduct research and critically assess recent developments and innovations in software testing.

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.

Course Outcome:

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.

Course Outcome:

CO1: Understand the fundamentals, architecture, and landscape of Blockchain technology.
CO2: Analyse blockchain applications and implementation strategies across various domains.
CO3: Explain the concepts of cryptocurrencies, wallets, and digital transactions.
CO4: Analyse blockchain consensus mechanisms and distributed ledger technologies.
CO5: Design enterprise blockchain solutions using appropriate platforms and frameworks.
CO6: Evaluate recent advances, emerging trends, and real-world applications of Blockchain technology.

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.

Course Outcome:

CO1: Deploy a private blockchain using Ethereum or Rust.
CO2: Implement a Bitcoin mining module solving proof-of-work in Rust.
CO3: Explore and evaluate alternative consensus mechanisms (PoS, PoSpace).
CO4: Compile and test smart contracts using EVM.
CO5: Deploy chaincode and create blockchain services using Hyperledger Fabric.
CO6: Deploy ERC20 tokens and launch tokens on alternative blockchains like BigchainDB.

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.

Course Outcome:

CO1: Implement the proposed research methodology to address the identified research problem.
CO2: Analyse and interpret the results obtained from the research work using appropriate techniques and tools.
CO3: Evaluate the research outcomes and validate the effectiveness of the proposed approach.
CO4: Prepare a comprehensive dissertation report documenting the research methodology, results, findings, and conclusions.
CO5: Prepare a research manuscript and effectively present and defend the research work during the final viva-voce.

Admissions Open 2026-27

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Course Curriculum

1ST SEMESTER SUBJECTS

Advanced Data Structure and Algorithm

Advanced Data Structure and Algorithm Lab

Research Methodology

Digital Image Processing

Digital Image Processing Lab

Advanced Operating System

Advanced Operating System Lab

Research Paper Writing and Publication Ethics

Cloud Infrastructure and Services Lab

CLOUD INFRASTRUCTURE AND SERVICES

Data Science

Data Science Lab

Continuous Delivery and DevOps

Continuous Delivery and DevOps Lab

2ND SEMESTER SUBJECTS

Advanced Computer Architecture

Advanced Database and Data Mining

Advanced Database and Data Mining Lab

Advanced Software Engineering

Advanced Machine Learning

Advanced Machine Learning Lab

Advanced Network Security

Project work

Full Stack Development

Full Stack Development Lab

Generative AI

Generative AI Lab

Internet of Things

Internet of Things Lab

3RD SEMESTER SUBJECTS

Deep Learning

DEEP LEARNING LAB

Compiler Design

Advanced Data Warehouse and Data Mining

Current Affairs

Dissertation-I

Software Testing & Quality Assurance

Software Testing & Quality Assurance Lab

Block chain Technology

Block Chain Technology Lab

4TH SEMESTER SUBJECTS

Dissertation-II

Program Outcomes & Features

Program Outcomes

Program Specific Outcomes

Admissions
Open 2026-27

Industry-oriented
learning

Modern campus
facilities

Academic excellence &
mentorship

Global opportunities &
placements