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.