Comprehensive Course Structure Table
| Semester | Course Code | Course Title | Credit (L-T-P-C) | Prerequisites |
|---|---|---|---|---|
| 1 | MAT101 | Calculus I | 3-1-0-4 | - |
| 1 | PHY101 | Physics I | 3-1-0-4 | - |
| 1 | CHE101 | Chemistry I | 3-1-0-4 | - |
| 1 | BIO101 | Biology I | 3-1-0-4 | - |
| 1 | ENG101 | English Communication | 2-0-0-2 | - |
| 1 | IT101 | Introduction to Programming | 2-0-2-3 | - |
| 1 | LAB101 | Programming Lab | 0-0-4-2 | - |
| 1 | MAT102 | Calculus II | 3-1-0-4 | MAT101 |
| 1 | PHY102 | Physics II | 3-1-0-4 | PHY101 |
| 1 | CHE102 | Chemistry II | 3-1-0-4 | CHE101 |
| 1 | BIO102 | Biology II | 3-1-0-4 | BIO101 |
| 1 | ENG102 | Technical Writing | 2-0-0-2 | - |
| 1 | IT102 | Data Structures and Algorithms | 3-0-0-3 | IT101 |
| 1 | LAB102 | Data Structures Lab | 0-0-4-2 | IT101 |
| 2 | MAT201 | Linear Algebra | 3-1-0-4 | MAT102 |
| 2 | PHY201 | Electromagnetism | 3-1-0-4 | PHY102 |
| 2 | CHE201 | Organic Chemistry | 3-1-0-4 | CHE102 |
| 2 | BIO201 | Genetics and Evolution | 3-1-0-4 | BIO102 |
| 2 | ENG201 | Communication Skills | 2-0-0-2 | - |
| 2 | IT201 | Database Management Systems | 3-0-0-3 | IT102 |
| 2 | LAB201 | Database Lab | 0-0-4-2 | IT102 |
| 2 | MAT202 | Differential Equations | 3-1-0-4 | MAT201 |
| 2 | PHY202 | Optics and Waves | 3-1-0-4 | PHY201 |
| 2 | CHE202 | Inorganic Chemistry | 3-1-0-4 | CHE201 |
| 2 | BIO202 | Microbiology | 3-1-0-4 | BIO201 |
| 2 | ENG202 | Professional Ethics | 2-0-0-2 | - |
| 2 | IT202 | Operating Systems | 3-0-0-3 | IT102 |
| 2 | LAB202 | Operating Systems Lab | 0-0-4-2 | IT102 |
| 3 | IT301 | Computer Networks | 3-0-0-3 | IT202 |
| 3 | LAB301 | Computer Networks Lab | 0-0-4-2 | IT202 |
| 3 | MAT301 | Probability and Statistics | 3-1-0-4 | MAT202 |
| 3 | PHY301 | Nuclear Physics | 3-1-0-4 | PHY202 |
| 3 | CHE301 | Physical Chemistry | 3-1-0-4 | CHE202 |
| 3 | BIO301 | Cell Biology | 3-1-0-4 | BIO202 |
| 3 | ENG301 | Research Methodology | 2-0-0-2 | - |
| 3 | IT302 | Software Engineering | 3-0-0-3 | IT202 |
| 3 | LAB302 | Software Engineering Lab | 0-0-4-2 | IT202 |
| 3 | MAT302 | Numerical Methods | 3-1-0-4 | MAT301 |
| 3 | PHY302 | Quantum Mechanics | 3-1-0-4 | PHY301 |
| 3 | CHE302 | Chemical Engineering Fundamentals | 3-1-0-4 | CHE301 |
| 3 | BIO302 | Biophysics | 3-1-0-4 | BIO301 |
| 3 | ENG302 | Leadership and Management | 2-0-0-2 | - |
| 3 | IT303 | Machine Learning | 3-0-0-3 | IT302 |
| 3 | LAB303 | Machine Learning Lab | 0-0-4-2 | IT302 |
| 4 | IT401 | Advanced Database Systems | 3-0-0-3 | IT201 |
| 4 | LAB401 | Advanced Database Lab | 0-0-4-2 | IT201 |
| 4 | MAT401 | Advanced Calculus | 3-1-0-4 | MAT302 |
| 4 | PHY401 | Relativity and Cosmology | 3-1-0-4 | PHY302 |
| 4 | CHE401 | Industrial Chemistry | 3-1-0-4 | CHE302 |
| 4 | BIO401 | Molecular Biology | 3-1-0-4 | BIO302 |
| 4 | ENG401 | Project Planning and Execution | 2-0-0-2 | - |
| 4 | IT402 | Distributed Systems | 3-0-0-3 | IT301 |
| 4 | LAB402 | Distributed Systems Lab | 0-0-4-2 | IT301 |
| 4 | MAT402 | Mathematical Modeling | 3-1-0-4 | MAT401 |
| 4 | PHY402 | Thermodynamics and Statistical Mechanics | 3-1-0-4 | PHY401 |
| 4 | CHE402 | Environmental Chemistry | 3-1-0-4 | CHE401 |
| 4 | BIO402 | Genetic Engineering | 3-1-0-4 | BIO401 |
| 4 | ENG402 | Ethics and Social Responsibility | 2-0-0-2 | - |
| 4 | IT403 | Artificial Intelligence | 3-0-0-3 | IT303 |
| 4 | LAB403 | AI Lab | 0-0-4-2 | IT303 |
Detailed Departmental Elective Courses
The department offers a wide array of advanced departmental electives designed to deepen students' understanding and skill sets in specialized areas. These courses are developed based on industry trends, academic advancements, and student interests.
- Deep Learning: This course explores neural network architectures, convolutional networks, recurrent networks, and transformer models. Students will implement algorithms using TensorFlow and PyTorch, gaining hands-on experience in image recognition, natural language processing, and time-series forecasting.
- Natural Language Processing: The focus is on building systems that understand and generate human language effectively. Topics include sentiment analysis, machine translation, chatbots, and text summarization using advanced NLP techniques like BERT and GPT models.
- Computer Vision: This course covers image processing, feature extraction, object detection, segmentation, and recognition tasks. Students will learn to build visual systems for autonomous vehicles, medical imaging, and surveillance applications.
- Cryptography and Network Security: A comprehensive overview of encryption algorithms, key management, digital signatures, and secure communication protocols. Practical sessions involve implementing security measures in real-world scenarios using tools like OpenSSL and Wireshark.
- Robotics and Control Systems: Introduces fundamental concepts in robotics, sensor integration, motion planning, and control theory. Students will design and simulate robotic systems for various applications including industrial automation and humanoid robots.
- Internet of Things (IoT): Covers the architecture of IoT networks, wireless communication protocols, embedded system programming, and cloud integration. Projects include smart home automation, environmental monitoring, and agricultural IoT solutions.
- Data Mining and Big Data Analytics: Focuses on extracting meaningful patterns from large datasets using techniques like clustering, classification, association rules, and anomaly detection. Students will use Hadoop, Spark, and Python libraries for scalable data processing.
- Embedded Systems Design: Teaches the design and implementation of embedded software and hardware systems for specific applications. Topics include microcontroller programming, real-time operating systems, and low-power optimization techniques.
- Reinforcement Learning: Explores decision-making processes in uncertain environments using algorithms like Q-learning, policy gradients, and actor-critic methods. Applications include game playing, autonomous navigation, and resource allocation.
- Cloud Computing and DevOps: Provides insights into cloud infrastructure, containerization technologies (Docker, Kubernetes), CI/CD pipelines, and microservices architecture. Students will deploy applications on AWS, Azure, or GCP platforms.
Project-Based Learning Philosophy
Our department strongly believes in project-based learning as a means to develop critical thinking, creativity, and practical problem-solving skills among students. The approach involves structured projects that span multiple semesters, allowing students to apply theoretical knowledge in real-world contexts.
The mandatory mini-projects are assigned at the end of each semester and serve as a foundation for the final-year thesis or capstone project. These projects encourage interdisciplinary collaboration, requiring students to work with peers from different engineering disciplines, fostering teamwork and communication skills.
Mini-projects typically last 4–6 weeks and involve selecting a relevant topic under faculty supervision. Students are expected to conduct literature review, design experiments, analyze results, and present findings through formal reports and oral presentations.
The final-year thesis or capstone project is a significant component of the program, lasting approximately 10–12 weeks. Students can choose from topics suggested by faculty members, industry partners, or their own research interests. The project must demonstrate originality, technical depth, and practical relevance.
Faculty mentors are assigned based on expertise alignment and student preferences. Regular meetings with mentors ensure progress tracking and timely resolution of challenges. Evaluation criteria include project planning, execution quality, innovation level, presentation effectiveness, and contribution to the field of study.