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M-TECH in Computer Science Engineering at Indian Institute of Science
Bengaluru, Karnataka
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About the Specialization
What is Computer Science & Engineering at Indian Institute of Science Bengaluru?
This Computer Science & Engineering M.Tech program at Indian Institute of Science Bengaluru focuses on advanced theoretical foundations and practical applications in computing. Leveraging IISc''''s strong research ethos, it nurtures expertise in cutting-edge areas highly relevant to India''''s burgeoning tech industry, from AI/ML to cybersecurity and high-performance computing.
Who Should Apply?
This program is ideal for engineering graduates (B.E./B.Tech.) in CS, IT, ECE, or related fields with a strong academic record and a valid GATE score, seeking to delve deeper into research and advanced technical roles. It also suits working professionals aiming to upskill and transition into leadership or specialized R&D positions within India''''s dynamic tech sector.
Why Choose This Course?
Graduates gain profound analytical and problem-solving skills, leading to high-demand careers in India as AI engineers, data scientists, software architects, research scientists, and cybersecurity experts. They are well-prepared for roles in top-tier Indian and multinational tech companies, with competitive salary packages and accelerated growth trajectories in the Indian IT landscape.
Student Success Practices
Foundation Stage
Master Core Theoretical Concepts- (Semester 1-2)
Diligently focus on understanding foundational subjects like Algorithms, Data Structures, Probability, and Discrete Math. These form the bedrock for advanced topics and problem-solving.
Tools & Resources
NPTEL lectures, GeeksforGeeks, HackerRank
Career Connection
Strong fundamentals are crucial for cracking technical interviews at product-based companies and excelling in advanced research roles.
Develop Strong Programming Skills- (Semester 1-2)
Complement theoretical learning with hands-on coding. Practice extensively in C++, Python, or Java to build robust problem-solving abilities and efficient code implementation.
Tools & Resources
LeetCode, Codeforces, Open-source projects
Career Connection
Essential for software development roles, data science positions, and implementing complex algorithms in research projects.
Engage with Faculty Research- (Semester 1-2)
Attend departmental seminars, explore faculty research profiles, and approach professors for opportunities to assist in their ongoing projects. This provides early exposure to research methodology.
Tools & Resources
IISc CSA department website, Research lab pages, Weekly colloquia and reading groups
Career Connection
Builds a strong foundation for thesis work, potential PhD aspirations, and R&D roles in both academia and industry.
Intermediate Stage
Deepen Specialization through Electives- (Semester 2-3)
Strategically choose elective courses that align with your career interests, such as AI/ML, Cybersecurity, or Distributed Systems. Focus on building in-depth knowledge in a niche area.
Tools & Resources
Course materials and advanced textbooks, Domain-specific online courses (e.g., Coursera, edX), Relevant research papers
Career Connection
Develops expertise for specialized technical roles and distinguishes candidates in India''''s competitive tech job market.
Actively Pursue Research Opportunities/Internships- (Semester 2 (summer) / Semester 3)
Seek summer research internships (SRI) at IISc or other top institutes, or industry internships that provide practical exposure to your chosen specialization, crucial for real-world problem-solving.
Tools & Resources
Departmental notices, Faculty connections, LinkedIn, Career fairs
Career Connection
Provides invaluable industry experience, strengthens your resume, and often leads to pre-placement offers (PPOs) from top companies in India.
Collaborate on Projects and Build a Portfolio- (Semester 2-3)
Work on substantial group projects or personal side projects to apply learned concepts. Contribute to open-source or build innovative applications, and document your work on platforms like GitHub.
Tools & Resources
GitHub, Kaggle (for data science projects), Project management tools, Departmental labs
Career Connection
Demonstrates practical skills to recruiters, builds a tangible portfolio of work, and serves as a strong talking point in job interviews.
Advanced Stage
Excel in M.Tech Project/Thesis Work- (Semester 3-4)
Dedicate significant effort to your M.Tech project. Define a clear problem, conduct thorough research, implement solutions, and rigorously evaluate your results. Aim for publication if possible.
Tools & Resources
Faculty mentorship, Research papers (IEEE Xplore, ACM Digital Library), Specialized software/hardware, LaTeX for thesis writing
Career Connection
The project is a major component for R&D roles, PhD applications, and showcasing independent research capability to industry leaders.
Intensive Placement Preparation- (Semester 3 (end) - Semester 4)
Begin focused preparation for placements well in advance. Practice mock interviews, solve advanced coding problems, and refine your resume and soft skills. Attend pre-placement talks and workshops.
Tools & Resources
InterviewBit, Glassdoor, LinkedIn, IISc''''s Career Development Centre, Peer groups for mock interviews
Career Connection
Maximizes chances of securing top-tier placements in Indian and global tech companies, aiming for roles like Software Engineer, Data Scientist, or Researcher.
Network with Industry Professionals and Alumni- (Semester 3-4)
Actively participate in departmental events, workshops, and conferences. Connect with IISc alumni and industry leaders to gain insights, explore opportunities, and build a professional network.
Tools & Resources
LinkedIn, IISc alumni network platforms, Industry meetups, Tech conferences
Career Connection
Opens doors to hidden job markets, mentorship opportunities, and long-term career growth in India''''s competitive tech landscape.
Program Structure and Curriculum
Eligibility:
- B.E./B.Tech. or equivalent degree in Computer Science, Information Technology, Electronics & Communication, or related engineering disciplines from a recognized university. A valid GATE score in CS/EC/EE/MA/ST is mandatory for admission.
Duration: 2 years (4 semesters)
Credits: Minimum 64 credits Credits
Assessment: Internal: undefined, External: undefined
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| E0 201 | Probability and Computing | Core | 3 | Probability spaces, Random variables, Conditional expectation, Law of large numbers, Central limit theorem, Randomized algorithms |
| E0 202 | Discrete Structures | Core | 3 | Logic and proofs, Set theory and relations, Functions and counting, Recurrence relations, Graph theory, Boolean algebra |
| E0 203 | Analysis and Design of Algorithms | Core | 3 | Algorithm analysis techniques, Sorting and searching, Graph algorithms, Greedy algorithms, Dynamic programming, NP-completeness |
| E0 204 | Computer Organization | Core | 3 | CPU design, Instruction sets, Memory hierarchy, Pipelining, Cache coherence, I/O organization |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| E0 205 | Operating Systems | Core | 3 | Processes and threads, CPU scheduling, Memory management, Virtual memory, File systems, Distributed operating systems concepts |
| E0 206 | Compiler Design | Core | 3 | Lexical analysis, Parsing techniques, Syntax-directed translation, Intermediate code generation, Code optimization, Runtime environments |
| E0 207 | Data Communication | Core | 3 | Digital communication fundamentals, Modulation and encoding, Error control coding, Multiplexing, Transmission media, Switching techniques |
| E0 208 | Computer Networks | Core | 3 | Network layering, TCP/IP protocol suite, Routing algorithms, Flow and congestion control, Application layer protocols, Network security basics |
| E0 209 | Database Management Systems | Core | 3 | Relational model and SQL, E-R diagrams, Normalization, Query processing and optimization, Transaction management, Concurrency control and recovery |
| E0 211 | Artificial Intelligence | Elective | 3 | Problem-solving and search, Knowledge representation, Logical reasoning, Uncertain knowledge and reasoning, Machine learning overview, Planning |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| E0 299 | M.Tech. Project (Part 1) | Project | 16 | Research methodology, Problem identification and literature survey, System design and architecture, Initial implementation and experimentation, Interim report writing, Technical presentation |
| E0 220 | Machine Learning | Elective | 3 | Supervised learning algorithms, Unsupervised learning algorithms, Deep learning basics, Reinforcement learning, Model evaluation and regularization, Applications of machine learning |
| E0 222 | Distributed Systems | Elective | 3 | Communication and RPC, Process management, Naming services, Consistency and replication, Fault tolerance, Distributed file systems |
| E0 216 | Cryptography and Network Security | Elective | 3 | Symmetric key cryptography, Public key cryptography, Hash functions and digital signatures, Authentication protocols, Network security applications, Security protocols |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| E0 299 | M.Tech. Project (Part 2) | Project | 16 | Advanced implementation and optimization, Extensive experimental evaluation, Result analysis and interpretation, Thesis writing and documentation, Final presentation and defense, Potential for research publication |
| E0 231 | Computer Vision | Elective | 3 | Image formation and processing, Feature detection and description, Object recognition and classification, Motion analysis and tracking, 3D vision and scene reconstruction, Deep learning for computer vision |
| E0 232 | Natural Language Processing | Elective | 3 | Language models, Part-of-speech tagging, Syntactic parsing, Named entity recognition, Machine translation, Sentiment analysis |
