Comprehensive Course Listing Across 8 Semesters
| Semester | Course Code | Course Title | Credit Structure (L-T-P-C) | Prerequisites |
|---|---|---|---|---|
| 1 | CSE101 | Introduction to Programming | 3-0-0-3 | - |
| 1 | CSE102 | Mathematics for Computer Science | 4-0-0-4 | - |
| 1 | CSE103 | Physics for Engineers | 3-0-0-3 | - |
| 1 | CSE104 | Engineering Graphics and Design | 2-0-0-2 | - |
| 1 | CSE105 | English for Engineers | 2-0-0-2 | - |
| 1 | CSE106 | Introduction to Computing | 3-0-0-3 | - |
| 2 | CSE201 | Data Structures and Algorithms | 4-0-0-4 | CSE101 |
| 2 | CSE202 | Discrete Mathematics | 3-0-0-3 | CSE102 |
| 2 | CSE203 | Digital Logic Design | 3-0-0-3 | - |
| 2 | CSE204 | Database Systems | 3-0-0-3 | CSE101 |
| 2 | CSE205 | Software Engineering | 3-0-0-3 | - |
| 2 | CSE206 | Computer Organization and Architecture | 3-0-0-3 | CSE103 |
| 3 | CSE301 | Operating Systems | 3-0-0-3 | CSE201 |
| 3 | CSE302 | Computer Networks | 3-0-0-3 | CSE204 |
| 3 | CSE303 | Compiler Design | 3-0-0-3 | CSE201 |
| 3 | CSE304 | Object-Oriented Programming | 3-0-0-3 | CSE101 |
| 3 | CSE305 | Linear Algebra and Numerical Methods | 3-0-0-3 | CSE102 |
| 3 | CSE306 | Artificial Intelligence | 3-0-0-3 | CSE201 |
| 4 | CSE401 | Machine Learning | 3-0-0-3 | CSE306 |
| 4 | CSE402 | Distributed Systems | 3-0-0-3 | CSE302 |
| 4 | CSE403 | Security in Computing | 3-0-0-3 | CSE204 |
| 4 | CSE404 | Web Technologies | 3-0-0-3 | CSE205 |
| 4 | CSE405 | Big Data Analytics | 3-0-0-3 | CSE204 |
| 4 | CSE406 | Embedded Systems | 3-0-0-3 | CSE203 |
| 5 | CSE501 | Advanced Algorithms | 3-0-0-3 | CSE201 |
| 5 | CSE502 | Cloud Computing | 3-0-0-3 | CSE402 |
| 5 | CSE503 | Natural Language Processing | 3-0-0-3 | CSE306 |
| 5 | CSE504 | Computer Vision | 3-0-0-3 | CSE306 |
| 5 | CSE505 | Blockchain Technologies | 3-0-0-3 | CSE403 |
| 5 | CSE506 | Human-Computer Interaction | 3-0-0-3 | - |
| 6 | CSE601 | Reinforcement Learning | 3-0-0-3 | CSE401 |
| 6 | CSE602 | Internet of Things (IoT) | 3-0-0-3 | CSE406 |
| 6 | CSE603 | Quantum Computing | 3-0-0-3 | CSE501 |
| 6 | CSE604 | Game Development | 3-0-0-3 | - |
| 6 | CSE605 | Network Security | 3-0-0-3 | CSE403 |
| 6 | CSE606 | Database Management Systems | 3-0-0-3 | CSE204 |
| 7 | CSE701 | Research Methodology | 2-0-0-2 | - |
| 7 | CSE702 | Capstone Project I | 4-0-0-4 | - |
| 8 | CSE801 | Capstone Project II | 4-0-0-4 | CSE702 |
| 8 | CSE802 | Internship | 4-0-0-4 | - |
Advanced Departmental Electives Overview
These advanced courses allow students to deepen their knowledge in specialized areas of interest. Each course is designed with a clear learning objective and relevance to current industry trends.
- Natural Language Processing (NLP): This course explores methods for processing and generating human language using computational models. Students learn about tokenization, parsing, sentiment analysis, and transformer-based architectures like BERT and GPT. The course includes practical implementation using Python libraries such as NLTK, spaCy, and Hugging Face Transformers.
- Computer Vision: Focused on image recognition, object detection, and visual understanding tasks, this course introduces students to convolutional neural networks (CNNs), transfer learning, and edge devices. Practical components include building models for facial recognition, autonomous vehicle navigation, and medical imaging applications.
- Reinforcement Learning: This course covers reinforcement algorithms, Markov Decision Processes, Q-learning, and policy gradients. Students apply these techniques to game-playing agents, robotic control systems, and recommendation engines using environments like OpenAI Gym and MuJoCo.
- Quantum Computing: Introduces the fundamentals of quantum mechanics and quantum algorithms. Topics include qubits, superposition, entanglement, and quantum circuits. Practical labs involve simulating quantum algorithms on platforms like Qiskit and Cirq.
- Internet of Things (IoT): Explores the architecture, protocols, and applications of IoT systems. Students design sensor networks, develop embedded firmware, and implement cloud integration for smart cities, agriculture, and healthcare.
- Blockchain Technologies: Covers blockchain consensus mechanisms, smart contracts, and decentralized applications (dApps). Practical sessions involve building Ethereum-based dApps using Solidity and Web3.js.
- Game Development: Combines programming, design, and creativity to build interactive games. Students learn game engines like Unity and Unreal, develop 2D/3D graphics, implement physics simulations, and optimize performance for various platforms.
- Human-Computer Interaction (HCI): Focuses on designing user interfaces that are intuitive and accessible. Topics include usability testing, cognitive psychology, accessibility standards, and prototyping tools like Figma and Sketch.
- Network Security: Examines vulnerabilities in networked systems and defensive strategies. Students explore firewall configurations, intrusion detection systems, secure coding practices, and penetration testing methodologies using tools like Wireshark and Metasploit.
- Database Management Systems: Covers relational database design, normalization, indexing, and query optimization. Practical labs involve designing complex schemas, implementing stored procedures, and managing distributed databases with PostgreSQL and MongoDB.
Project-Based Learning Philosophy
The department strongly believes in experiential learning through project-based education. Students engage in both individual and team-based projects that simulate real-world challenges. These projects span from concept development to deployment, encouraging innovation and collaboration.
Mini-projects begin in the second year, where students tackle small-scale problems under faculty guidance. These projects are assessed based on technical execution, documentation quality, presentation skills, and peer evaluations. The capstone project in the final year is a substantial endeavor involving multidisciplinary teams working on solutions to societal or industry-specific issues.
Students select their projects based on personal interest, faculty availability, and alignment with current research areas. Faculty mentors are assigned based on expertise and project relevance. Regular progress meetings ensure timely delivery and quality outcomes. The final submission includes a detailed report, live demonstration, and oral defense before a panel of experts.