Course Structure Overview
| Semester | Course Code | Course Title | Credit Structure (L-T-P-C) | Prerequisites |
|---|---|---|---|---|
| Semester I | ENG101 | English for Engineers | 3-0-0-3 | - |
| MAT101 | Mathematics I | 4-0-0-4 | - | |
| PHY101 | Physics for Engineers | 3-0-0-3 | - | |
| CHE101 | Chemistry for Engineers | 3-0-0-3 | - | |
| BIO101 | Biology for Engineers | 2-0-0-2 | - | |
| CSE101 | Introduction to Programming | 2-0-2-3 | - | |
| ENG102 | Engineering Drawing & Design | 1-0-3-2 | - | |
| MAT102 | Mathematics II | 4-0-0-4 | MAT101 | |
| PHY102 | Physics II | 3-0-0-3 | PHY101 | |
| CSE102 | Data Structures & Algorithms | 3-0-0-3 | CSE101 | |
| Semester II | MAT201 | Mathematics III | 4-0-0-4 | MAT102 |
| ECE201 | Basic Electronics | 3-0-0-3 | - | |
| MAT202 | Probability & Statistics | 3-0-0-3 | MAT102 | |
| ENG201 | Engineering Mechanics | 3-0-0-3 | - | |
| CSE201 | Object-Oriented Programming | 3-0-0-3 | CSE102 | |
| MAT203 | Linear Algebra | 3-0-0-3 | MAT201 | |
| CHE201 | Organic Chemistry | 3-0-0-3 | CHE101 | |
| BIO201 | Cell Biology | 3-0-0-3 | BIO101 | |
| PHY201 | Electromagnetic Fields | 3-0-0-3 | PHY102 | |
| ENG202 | Computer Architecture | 3-0-0-3 | CSE201 | |
| Semester III | ECE301 | Signals & Systems | 3-0-0-3 | ECE201 |
| MAT301 | Differential Equations | 3-0-0-3 | MAT201 | |
| CSE301 | Database Management Systems | 3-0-0-3 | CSE201 | |
| MEC301 | Mechanics of Materials | 3-0-0-3 | ENG201 | |
| CIV301 | Strength of Materials | 3-0-0-3 | ENG201 | |
| EEE301 | Electrical Circuits & Networks | 3-0-0-3 | ECE201 | |
| MAT302 | Numerical Methods | 3-0-0-3 | MAT201 | |
| BIO301 | Genetics & Molecular Biology | 3-0-0-3 | BIO201 | |
| CHE301 | Physical Chemistry | 3-0-0-3 | CHE201 | |
| ENG301 | Design & Manufacturing | 2-0-2-2 | - | |
| Semester IV | CSE401 | Operating Systems | 3-0-0-3 | CSE201 |
| ECE401 | Analog & Digital Electronics | 3-0-0-3 | ECE301 | |
| MEC401 | Thermodynamics | 3-0-0-3 | ENG201 | |
| CIV401 | Structural Analysis | 3-0-0-3 | CIV301 | |
| EEE401 | Electromagnetic Fields & Waves | 3-0-0-3 | PHY201 | |
| MAT401 | Complex Analysis | 3-0-0-3 | MAT301 | |
| CHE401 | Inorganic Chemistry | 3-0-0-3 | CHE301 | |
| BIO401 | Biostatistics & Bioinformatics | 3-0-0-3 | BIO301 | |
| ENG401 | Industrial Engineering | 3-0-0-3 | - | |
| CSE402 | Web Technologies | 3-0-0-3 | CSE401 | |
| Semester V | CSE501 | Machine Learning | 3-0-0-3 | CSE401 |
| ECE501 | Communication Systems | 3-0-0-3 | ECE401 | |
| MEC501 | Fluid Mechanics | 3-0-0-3 | ENG201 | |
| CIV501 | Geotechnical Engineering | 3-0-0-3 | CIV401 | |
| EEE501 | Power Electronics | 3-0-0-3 | EEE401 | |
| MAT501 | Advanced Calculus | 3-0-0-3 | MAT401 | |
| CHE501 | Chemical Kinetics | 3-0-0-3 | CHE401 | |
| BIO501 | Microbiology & Immunology | 3-0-0-3 | BIO401 | |
| ENG501 | Project Management | 2-0-0-2 | - | |
| CSE502 | Computer Vision | 3-0-0-3 | CSE501 | |
| Semester VI | CSE601 | Deep Learning | 3-0-0-3 | CSE501 |
| ECE601 | VLSI Design | 3-0-0-3 | ECE501 | |
| MEC601 | Heat Transfer | 3-0-0-3 | MEC401 | |
| CIV601 | Transportation Engineering | 3-0-0-3 | CIV501 | |
| EEE601 | Control Systems | 3-0-0-3 | EEE401 | |
| MAT601 | Optimization Techniques | 3-0-0-3 | MAT501 | |
| CHE601 | Industrial Chemistry | 3-0-0-3 | CHE501 | |
| BIO601 | Biotechnology Applications | 3-0-0-3 | BIO501 | |
| ENG601 | Entrepreneurship | 2-0-0-2 | - | |
| CSE602 | Blockchain Technology | 3-0-0-3 | CSE501 | |
| Semester VII | CSE701 | Advanced Algorithms | 3-0-0-3 | CSE601 |
| ECE701 | Antenna & Wave Propagation | 3-0-0-3 | ECE601 | |
| MEC701 | Advanced Manufacturing | 3-0-0-3 | MEC601 | |
| CIV701 | Environmental Engineering | 3-0-0-3 | CIV601 | |
| EEE701 | Renewable Energy Systems | 3-0-0-3 | EEE601 | |
| MAT701 | Applied Mathematics | 3-0-0-3 | MAT601 | |
| CHE701 | Chemical Process Design | 3-0-0-3 | CHE601 | |
| BIO701 | Bioinformatics & Computational Biology | 3-0-0-3 | BIO601 | |
| ENG701 | Capstone Project I | 2-0-2-2 | - | |
| CSE702 | Software Architecture & Design Patterns | 3-0-0-3 | CSE601 | |
| Semester VIII | CSE801 | Capstone Project II | 4-0-2-3 | ENG701 |
| ECE801 | RF & Microwave Engineering | 3-0-0-3 | ECE701 | |
| MEC801 | Robotics & Automation | 3-0-0-3 | MEC701 | |
| CIV801 | Urban Planning & Design | 3-0-0-3 | CIV701 | |
| EEE801 | Smart Grid Technologies | 3-0-0-3 | EEE701 | |
| MAT801 | Mathematical Modeling | 3-0-0-3 | MAT701 | |
| CHE801 | Advanced Catalysis | 3-0-0-3 | CHE701 | |
| BIO801 | Systems Biology | 3-0-0-3 | BIO701 | |
| ENG801 | Innovation & Leadership | 2-0-0-2 | - | |
| CSE802 | Cloud Computing | 3-0-0-3 | CSE701 |
Advanced Departmental Elective Courses
Deep Learning (CSE501): This course introduces students to neural network architectures, backpropagation algorithms, and deep learning frameworks like TensorFlow and PyTorch. It emphasizes practical implementation through hands-on projects involving image classification, natural language processing, and reinforcement learning. Students also explore real-world applications in autonomous vehicles and medical diagnosis systems.
Computer Vision (CSE502): Designed to build expertise in analyzing visual data using computer algorithms, this course covers topics such as feature extraction, object detection, image segmentation, and facial recognition technologies. Through project-based learning, students implement advanced vision models using tools like OpenCV and Python libraries.
Blockchain Technology (CSE602): This elective explores the principles of distributed ledger technology, smart contracts, and cryptocurrency systems. Students learn to develop decentralized applications (dApps) using Ethereum and Hyperledger platforms, gaining insights into blockchain security protocols and consensus mechanisms.
Software Architecture & Design Patterns (CSE702): Focuses on scalable software design principles, microservices architecture, API development, and enterprise-level application deployment. Students engage in designing and building complex software systems using industry-standard tools and frameworks such as Docker, Kubernetes, and Spring Boot.
Artificial Intelligence in Robotics (CSE801): Combines AI concepts with robotics engineering to enable autonomous behavior in robotic systems. Topics include sensor integration, motion planning, pathfinding algorithms, and machine learning applications for robot control and interaction with environments.
Advanced Algorithms (CSE701): This course delves into complex algorithmic techniques including dynamic programming, graph theory, approximation algorithms, and computational complexity analysis. Students apply these concepts to solve challenging problems in data science, network optimization, and cryptography.
Cloud Computing (CSE802): Covers cloud infrastructure models, virtualization technologies, container orchestration, and multi-cloud strategies. Students gain experience with AWS, Azure, and Google Cloud Platform services while building scalable applications that leverage distributed computing resources.
Quantum Computing & Cryptography (CSE803): An emerging field combining quantum physics with information technology, this course explores qubit manipulation, quantum algorithms, and quantum key distribution. Students experiment with quantum simulators and explore future applications in secure communications and computational modeling.
Project-Based Learning Philosophy
The department strongly believes that experiential learning is crucial for developing well-rounded engineers capable of solving real-world problems. Project-based learning forms the core of our curriculum, starting from early semesters with mini-projects and culminating in a comprehensive final-year thesis or capstone project.
Mini-Projects
Each student undertakes at least two mini-projects during their academic journey—one in the second year and another in the fourth year. These projects are designed to reinforce theoretical knowledge with practical skills, encouraging innovation and teamwork. Mini-projects typically last 8–10 weeks and involve working in teams of 3–5 members under faculty supervision.
Final-Year Thesis/Capstone Project
The final-year project is a significant milestone that requires students to demonstrate mastery in their chosen specialization area. The project must address a relevant societal or industrial challenge, involving extensive literature review, data collection, analysis, and implementation of solutions. Students select their projects based on faculty research interests or industry collaborations, ensuring relevance and impact.
Selection Process
Students are paired with faculty mentors based on mutual interest areas and availability. Mentors guide students through the entire process—from defining project scope to preparing presentations and documentation. The selection is done through a transparent online portal where students submit proposals, and mentors provide feedback before final allocation.
Evaluation Criteria
Projects are evaluated based on several criteria including technical depth, innovation, presentation quality, documentation standards, teamwork, and adherence to deadlines. A panel of experts including faculty members and external reviewers assesses each project at mid-term and final stages, providing constructive feedback for improvement.