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M-TECH in Computer Science And Engineering at National Institute of Technology Karnataka, Surathkal
Dakshina Kannada, Karnataka
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About the Specialization
What is Computer Science and Engineering at National Institute of Technology Karnataka, Surathkal Dakshina Kannada?
This M.Tech Computer Science and Engineering program at National Institute of Technology Karnataka, Mangaluru, focuses on advanced theoretical and applied aspects of computing. It addresses the growing need for highly skilled professionals in India''''s technology sector, preparing graduates for cutting-edge roles in software development, AI, data science, and cybersecurity. The program emphasizes research, innovation, and practical problem-solving relevant to Indian industries.
Who Should Apply?
This program is ideal for engineering graduates with a B.E./B.Tech in Computer Science, IT, or related fields, holding a valid GATE score, who aspire to deepen their technical expertise. It attracts fresh graduates seeking entry into R&D roles in IT giants or startups, as well as working professionals aiming to upskill for leadership positions in emerging technologies within the Indian market. Passion for advanced computing and problem-solving is crucial.
Why Choose This Course?
Graduates of this program can expect diverse career paths in India, including roles as AI/ML Engineers, Data Scientists, Cloud Architects, Cybersecurity Specialists, or Software Development Engineers. Entry-level salaries typically range from INR 8-15 LPA, with experienced professionals earning significantly higher. The program also prepares students for PhD research or specialized roles in PSUs and government organizations, contributing to India''''s digital transformation.
Student Success Practices
Foundation Stage
Master Advanced Data Structures & Algorithms- (Semester 1)
Dedicate significant time to understanding and implementing complex data structures and algorithmic paradigms. Participate in coding challenges regularly to strengthen problem-solving abilities.
Tools & Resources
HackerRank, LeetCode, CodeChef, GeeksforGeeks, NPTEL courses
Career Connection
Strong DSA skills are fundamental for cracking technical interviews at top product-based companies and excelling in competitive programming, crucial for landing high-paying tech jobs in India.
Deep Dive into Core Systems Concepts- (Semester 1)
Actively engage with subjects like Computer System Architecture and Advanced Operating Systems. Understand system interactions, performance, and security mechanisms by experimenting with lab assignments and projects beyond basic requirements.
Tools & Resources
OS simulation tools, virtualization platforms, documentation of Linux kernel/Windows internals
Career Connection
Essential for roles in system design, backend development, and performance engineering, highly sought after in Indian tech companies like Intel, Qualcomm, and various startups.
Explore Machine Learning Fundamentals- (Semester 1)
Beyond coursework, implement basic ML algorithms from scratch using Python libraries like NumPy. Participate in online ML competitions or complete introductory projects to build practical exposure.
Tools & Resources
Kaggle, Coursera (Andrew Ng''''s ML course), TensorFlow, PyTorch, Scikit-learn
Career Connection
Builds a strong base for AI/ML engineering, data science, and research roles, which are high-demand and rapidly growing areas in India''''s technology landscape.
Intermediate Stage
Specialize through Electives & Projects- (Semester 2)
Carefully choose electives that align with your career goals (e.g., Deep Learning, Cloud Computing, Cybersecurity). Start working on mini-projects related to these specialized areas to develop a focused skill set.
Tools & Resources
Relevant open-source libraries, domain-specific tools, collaboration platforms like GitHub
Career Connection
Develops a niche skillset and a compelling portfolio, making you a more attractive candidate for specialized roles in companies like Google, Microsoft, or Indian AI startups.
Network and Seek Internship Opportunities- (Semester 2)
Attend webinars, conferences, and workshops. Connect with alumni and industry professionals through LinkedIn. Actively apply for summer research internships or industry internships to gain practical exposure and build contacts.
Tools & Resources
LinkedIn, NITK alumni network, placement cell, professional body events and conferences
Career Connection
Internships are often a direct path to pre-placement offers (PPOs) and provide invaluable real-world experience, significantly boosting placement chances in Indian and MNCs.
Refine Research and Technical Writing Skills- (Semester 2)
Focus on the Research Methodology course. Practice technical writing by drafting reports, literature reviews, and potentially submitting a paper to a student conference or workshop to hone academic communication.
Tools & Resources
LaTeX, Zotero/Mendeley, academic writing guides, peer review with seniors/professors
Career Connection
Crucial for academic careers, R&D positions, and effectively communicating complex technical ideas in any professional setting, an undervalued skill in many Indian graduates.
Advanced Stage
Execute a High-Impact M.Tech Project- (Semesters 3-4)
Devote significant effort to your Project Work (Phase I & II). Aim for a novel contribution, publishable results, or a solution with significant industrial application. Work closely with your advisor and potentially industry mentors.
Tools & Resources
Advanced simulation software, high-performance computing resources (if needed), research labs at NITK, industrial collaborators
Career Connection
A strong M.Tech project acts as a powerful portfolio piece, demonstrating problem-solving capabilities, technical depth, and research aptitude to potential employers or PhD committees.
Ace Placements and Career Planning- (Semesters 3-4)
Participate diligently in mock interviews, group discussions, and aptitude tests offered by the placement cell. Prepare a compelling resume and LinkedIn profile. Network actively for job referrals and leverage alumni connections.
Tools & Resources
NITK Placement Cell, InterviewBit, Glassdoor, personal mentors from industry
Career Connection
Directly leads to successful placements in desired companies and helps in building a strategic long-term career roadmap, aligning personal goals with Indian market opportunities.
Continuous Learning and Skill Enhancement- (Semesters 3-4)
Stay updated with the latest advancements in your chosen specialization by reading research papers, attending online courses, and contributing to open-source projects. Consider pursuing industry certifications (e.g., AWS, Azure, Google Cloud).
Tools & Resources
Coursera, edX, Udemy, official cloud provider documentation, GitHub
Career Connection
Ensures lifelong employability and adaptability in a rapidly evolving tech landscape, making you a valuable asset in the Indian and global job market by maintaining relevance.
Program Structure and Curriculum
Eligibility:
- B.E./B.Tech. in Computer Science and Engineering/ Information Technology or equivalent degree with minimum 6.5 CGPA (60% marks) for GEN/GEN-EWS/OBC-NCL (6.0 CGPA / 55% for SC/ST/PwD) and a valid GATE score in CS.
Duration: 4 semesters / 2 years
Credits: 67 Credits
Assessment: Internal: 50%, External: 50%
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CSP601 | Advanced Data Structures and Algorithms | Core | 3 | Review of Data Structures, Hashing Techniques, Advanced Trees and Heaps, Graph Algorithms, Algorithm Design Techniques, Complexity Analysis |
| CSP602 | Computer System Architecture | Core | 3 | CPU Design and Pipelining, Memory Hierarchy, Input/Output Organization, Instruction Level Parallelism, Multiprocessors and Thread Level Parallelism, Vector Processors |
| CSP603 | Advanced Operating Systems | Core | 3 | OS Structures and Processes, Distributed Operating Systems, Inter-Process Communication, Distributed Synchronization, Distributed File Systems, Security and Protection |
| CSP604 | Machine Learning | Core | 3 | Introduction to Machine Learning, Supervised Learning (Regression, Classification), Unsupervised Learning (Clustering, PCA), Neural Networks and Deep Learning Basics, Ensemble Methods, Reinforcement Learning |
| CSP6XX | Program Elective - I | Elective | 3 | Topics vary based on chosen elective from the list of Program Electives available. |
| CSP605 | Advanced Data Structures and Algorithms Lab | Lab | 3 | Implementation of Hashing, Tree and Graph Traversals, Dynamic Programming Applications, Shortest Path Algorithms, Network Flow Problems, Advanced Sorting Techniques |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CSP651 | Advanced Software Engineering | Core | 3 | Software Process Models, Requirements Engineering, Software Design Patterns, Software Testing and Quality Assurance, Software Project Management, Agile and DevOps Methodologies |
| CSP652 | Advanced Computer Networks | Core | 3 | Network Architectures and Protocols, Routing Algorithms, Transport Layer Protocols, Congestion Control, Wireless and Mobile Networks, Network Security Concepts |
| CSP653 | Research Methodology and IPR | Core | 3 | Research Problem Identification, Literature Review Techniques, Research Design and Methods, Data Collection and Analysis, Report Writing and Presentation, Intellectual Property Rights |
| CSP6XX | Program Elective - II | Elective | 3 | Topics vary based on chosen elective from the list of Program Electives available. |
| CSP6XX | Program Elective - III | Elective | 3 | Topics vary based on chosen elective from the list of Program Electives available. |
| CSP655 | Advanced Software Engineering Lab | Lab | 3 | UML Modeling Tools, Design Pattern Implementation, Software Testing Frameworks, Version Control Systems, Agile Development Practices, Software Project Management Tools |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CSP700 | Project Work - Phase I | Project | 7 | Problem Identification, Literature Survey, Project Proposal Formulation, System Design and Architecture, Initial Implementation and Experimentation, Progress Report and Presentation |
| CSP7XX | Program Elective - IV | Elective | 3 | Topics vary based on chosen elective from the list of Program Electives available. |
| CSP7XX | Program Elective - V | Elective | 3 | Topics vary based on chosen elective from the list of Program Electives available. |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CSP750 | Project Work - Phase II | Project | 15 | Advanced System Implementation, Extensive Testing and Validation, Performance Analysis and Optimization, Thesis Writing and Documentation, Final Presentation and Demonstration, Project Defense |
Semester courses
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CSP611 | Advanced Databases | Elective | 3 | Data Models and Query Languages, Query Processing and Optimization, Transaction Management and Concurrency Control, Distributed Databases, NoSQL Databases, Big Data Storage Systems |
| CSP612 | Data Mining | Elective | 3 | Data Preprocessing and Exploration, Association Rule Mining, Classification Techniques, Clustering Algorithms, Anomaly Detection, Web and Text Mining |
| CSP613 | Cryptography and Network Security | Elective | 3 | Classical Cryptography, Symmetric-Key Cryptography, Asymmetric-Key Cryptography, Hash Functions and Digital Signatures, Network Security Protocols (SSL/TLS, IPsec), Intrusion Detection Systems |
| CSP614 | Information Retrieval | Elective | 3 | Boolean Model, Vector Space Model, Inverted Indexing, Text Preprocessing, Relevance Feedback, Evaluation Metrics, Web Search and Ranking |
| CSP615 | Digital Image Processing | Elective | 3 | Image Enhancement, Image Restoration, Image Compression, Image Segmentation, Feature Extraction, Color Image Processing |
| CSP616 | IoT and Sensor Networks | Elective | 3 | IoT Architecture, Sensor Network Protocols, IoT Communication Technologies, Data Analytics for IoT, Security and Privacy in IoT, IoT Applications |
| CSP617 | Big Data Analytics | Elective | 3 | Introduction to Big Data, Hadoop Ecosystem (HDFS, MapReduce), Spark and Stream Processing, NoSQL Databases for Big Data, Big Data Storage and Management, Big Data Analytics Techniques |
| CSP618 | Web Technologies | Elective | 3 | Web Architecture and Protocols, Client-Side Scripting (HTML, CSS, JavaScript), Server-Side Programming (Node.js, Python, PHP), Database Integration, Web Security, Web Services and APIs |
| CSP619 | Cloud Computing | Elective | 3 | Cloud Computing Architecture, Virtualization Technologies, Cloud Service Models (IaaS, PaaS, SaaS), Cloud Deployment Models, Cloud Storage and Networking, Cloud Security and Management |
| CSP620 | Block Chain Technology | Elective | 3 | Cryptography in Blockchain, Distributed Ledger Technologies, Consensus Mechanisms, Smart Contracts, Blockchain Platforms (Ethereum, Hyperledger), Blockchain Applications |
| CSP621 | Cyber Physical Systems | Elective | 3 | Introduction to CPS, Modeling and Design of CPS, Control and Communication in CPS, Sensing and Actuation, Security and Privacy in CPS, CPS Applications |
| CSP622 | Deep Learning | Elective | 3 | Neural Network Architectures, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs), Deep Reinforcement Learning, Deep Learning Frameworks (TensorFlow, PyTorch) |
| CSP623 | Natural Language Processing | Elective | 3 | Text Preprocessing and Tokenization, Part-of-Speech Tagging, Syntactic and Semantic Analysis, Word Embeddings, Machine Translation, Sentiment Analysis |
| CSP624 | Information Security | Elective | 3 | Security Principles and Policies, Authentication and Authorization, Access Control Models, Malware and Vulnerabilities, Risk Management, Security Auditing |
| CSP625 | High Performance Computing | Elective | 3 | Parallel Computing Architectures, Distributed Memory Programming (MPI), Shared Memory Programming (OpenMP), GPU Computing (CUDA, OpenCL), Performance Measurement and Optimization, Cluster Computing |
| CSP626 | Compiler Design | Elective | 3 | Lexical Analysis, Syntax Analysis (Parsing), Semantic Analysis, Intermediate Code Generation, Code Optimization, Target Code Generation |
| CSP627 | Real Time Systems | Elective | 3 | Real-Time Concepts and Issues, Real-Time Scheduling, Real-Time Operating Systems, Resource Management, Real-Time Communication, Fault Tolerance in RTS |
| CSP628 | Human Computer Interaction | Elective | 3 | HCI Fundamentals, User Interface Design Principles, Usability Evaluation, Interaction Design Models, Cognitive Aspects of HCI, Emerging Technologies and HCI |
| CSP629 | Computer Vision | Elective | 3 | Image Formation and Filtering, Feature Detection and Description, Image Segmentation, Motion Estimation, Object Recognition, 3D Vision |
| CSP630 | Software Defined Networks | Elective | 3 | SDN Architecture, OpenFlow Protocol, SDN Controllers, Network Virtualization, Network Functions Virtualization (NFV), SDN Applications and Use Cases |
| CSP631 | Data Science | Elective | 3 | Data Collection and Cleaning, Exploratory Data Analysis, Statistical Modeling, Machine Learning for Data Science, Data Visualization, Big Data Tools |
| CSP632 | Cognitive Computing | Elective | 3 | Introduction to Cognitive Computing, Cognitive Architectures, Natural Language Processing for Cognition, Machine Learning and Reasoning, Cognitive Systems Design, Human-Cognitive System Interaction |
| CSP633 | Digital Forensics | Elective | 3 | Introduction to Digital Forensics, Evidence Collection and Preservation, File System Forensics, Network Forensics, Mobile Device Forensics, Legal and Ethical Aspects |
| CSP634 | Ethical Hacking | Elective | 3 | Hacking Phases and Methodologies, Footprinting and Reconnaissance, Scanning Networks, System Hacking, Malware Threats, Web Application Hacking |
| CSP635 | Reinforcement Learning | Elective | 3 | Markov Decision Processes, Dynamic Programming in RL, Monte Carlo Methods, Temporal-Difference Learning, Function Approximation, Deep Reinforcement Learning |
| CSP636 | Genetic Algorithms and Fuzzy Systems | Elective | 3 | Introduction to Optimization, Genetic Algorithms, Fuzzy Logic and Fuzzy Sets, Fuzzy Inference Systems, Neuro-Fuzzy Systems, Applications of Soft Computing |
| CSP637 | Quantum Computing | Elective | 3 | Quantum Mechanics Basics, Qubits and Quantum Gates, Quantum Algorithms (Shor''''s, Grover''''s), Quantum Entanglement, Quantum Cryptography, Quantum Computing Hardware |
| CSP638 | Robotics | Elective | 3 | Robot Kinematics and Dynamics, Robot Control Architectures, Sensors and Actuators, Robot Vision, Path Planning and Navigation, Human-Robot Interaction |
| CSP639 | Bio-Inspired Computing | Elective | 3 | Swarm Intelligence (PSO, ACO), Ant Colony Optimization, Artificial Immune Systems, Neural Networks (revisited), Evolutionary Computation, Biologically Inspired Optimization |
| CSP640 | Social Network Analysis | Elective | 3 | Graph Theory Basics for Networks, Centrality Measures, Community Detection, Network Models, Information Diffusion, Social Media Mining |
| CSP641 | GPU Computing | Elective | 3 | GPU Architecture, CUDA Programming Model, OpenCL Programming, Memory Management on GPU, Performance Optimization, GPU Applications |
| CSP642 | Mobile Computing | Elective | 3 | Mobile Communication Systems, Wireless LANs, Mobile IP and Ad-Hoc Networks, Mobile Operating Systems, Mobile Application Development, Location-Based Services |
| CSP643 | Wireless Sensor Networks | Elective | 3 | WSN Architecture, Sensor Node Hardware, MAC Protocols for WSN, Routing Protocols for WSN, Localization and Time Synchronization, Security in WSN |
| CSP644 | Fault Tolerant Systems | Elective | 3 | Fault Models and Metrics, Redundancy Techniques, Error Detection and Correction, Fault-Tolerant Architectures, Software Fault Tolerance, Reliability and Availability Analysis |
| CSP645 | Green Computing | Elective | 3 | Energy Efficiency in Computing, Green IT Strategies, Sustainable Data Centers, Power Management in Hardware, Green Software Development, Environmental Impact of IT |
| CSP646 | Enterprise Computing | Elective | 3 | Enterprise Architectures, Distributed Computing Frameworks, Middleware Technologies, Enterprise Application Integration, Cloud-Native Enterprise Applications, Enterprise Security |
| CSP647 | Data Warehousing | Elective | 3 | Data Warehouse Architecture, Dimensional Modeling (Star, Snowflake Schemas), ETL Process (Extraction, Transformation, Loading), OLAP Operations, Data Cube Technology, Data Warehouse Design and Implementation |
| CSP648 | Pattern Recognition | Elective | 3 | Feature Extraction and Selection, Statistical Pattern Recognition, Neural Pattern Recognition, Clustering Algorithms (revisited), Support Vector Machines, Hidden Markov Models |
| CSP649 | Game Theory | Elective | 3 | Strategic Form Games, Extensive Form Games, Nash Equilibrium, Cooperative Games, Mechanism Design, Applications in Computer Science |
| CSP650 | Parallel and Distributed Algorithms | Elective | 3 | Models of Parallel Computation, Parallel Algorithm Design Techniques, Distributed Shared Memory, Distributed Consensus, Fault Tolerance in Distributed Systems, Graph Algorithms for Distributed Systems |
| CSP651 | Soft Computing | Elective | 3 | Fuzzy Logic and Fuzzy Sets, Artificial Neural Networks, Genetic Algorithms, Hybrid Systems, Rough Sets, Applications in Optimization |
| CSP652 | Computer Graphics | Elective | 3 | Graphics Primitives and Rasterization, 2D and 3D Transformations, Clipping and Projections, Shading and Rendering Techniques, Texture Mapping, Animation |
| CSP653 | Artificial Intelligence | Elective | 3 | Problem Solving by Search, Knowledge Representation, Logical Reasoning, Planning, Uncertainty and Probabilistic Reasoning, Machine Learning Fundamentals |
| CSP654 | Advanced Computer Networks | Elective | 3 | Network Architectures and Protocols, Routing Algorithms, Transport Layer Protocols, Congestion Control, Wireless and Mobile Networks, Network Security Concepts |
