Course Breakdown Across 8 Semesters

Semester	Course Code	Course Title	Credit Structure (L-T-P-C)	Prerequisites
1	MATH101	Calculus I	3-1-0-4	None
1	PHYS101	Physics I	3-1-0-4	None
1	CHEM101	Chemistry I	3-1-0-4	None
1	ENGL101	English Communication Skills	2-0-0-2	None
1	CSE101	Introduction to Programming	3-1-0-4	None
2	MATH102	Calculus II	3-1-0-4	MATH101
2	PHYS102	Physics II	3-1-0-4	PHYS101
2	MATH201	Linear Algebra	3-1-0-4	MATH101
2	CSE102	Data Structures & Algorithms	3-1-0-4	CSE101
2	ECE101	Basic Electrical Circuits	3-1-0-4	None
3	MATH202	Differential Equations	3-1-0-4	MATH102
3	STAT201	Probability & Statistics	3-1-0-4	MATH102
3	CSE201	Database Management Systems	3-1-0-4	CSE102
3	ECE201	Signals & Systems	3-1-0-4	ECE101
3	CIV201	Engineering Mechanics	3-1-0-4	PHYS102
4	MATH203	Numerical Methods	3-1-0-4	MATH201
4	CSE202	Operating Systems	3-1-0-4	CSE102
4	ECE202	Digital Electronics	3-1-0-4	ECE101
4	CIV202	Strength of Materials	3-1-0-4	CIV201
5	MATH301	Complex Variables	3-1-0-4	MATH202
5	CSE301	Computer Networks	3-1-0-4	CSE202
5	ECE301	Analog Electronics	3-1-0-4	ECE202
5	CIV301	Structural Analysis	3-1-0-4	CIV202
6	MATH302	Transform Methods	3-1-0-4	MATH301
6	CSE302	Software Engineering	3-1-0-4	CSE301
6	ECE302	Microprocessors	3-1-0-4	ECE301
6	CIV302	Transportation Engineering	3-1-0-4	CIV301
7	MATH401	Optimization Techniques	3-1-0-4	MATH302
7	CSE401	Machine Learning	3-1-0-4	CSE302
7	ECE401	Control Systems	3-1-0-4	ECE302
7	CIV401	Environmental Engineering	3-1-0-4	CIV302
8	CSE402	Capstone Project	3-1-0-4	CSE401
8	ECE402	Final Year Project	3-1-0-4	ECE401
8	CIV402	Final Year Design	3-1-0-4	CIV401

Advanced Departmental Elective Courses

Departmental electives provide students with opportunities to explore specialized areas within their chosen field of engineering. These courses are designed to deepen understanding and foster innovation through advanced topics and practical applications.

• Advanced Machine Learning: This course delves into deep learning architectures, reinforcement learning, and generative models. Students learn to implement complex algorithms using frameworks like TensorFlow and PyTorch while working on real-world datasets.
• Quantum Computing Fundamentals: Introduces students to quantum bits, superposition, entanglement, and quantum algorithms. The course includes hands-on experience with quantum simulators and explores applications in cryptography and optimization.
• Renewable Energy Systems Design: Focuses on designing solar, wind, and hydroelectric systems for optimal efficiency. Students engage in modeling and simulation using tools like MATLAB/Simulink and participate in community-based renewable energy projects.
• Bioinformatics & Computational Biology: Combines biology with computer science to analyze biological data. Topics include genome sequencing, protein structure prediction, and drug discovery algorithms, supported by computational tools and databases.
• Smart Grid Technologies: Covers the integration of distributed energy resources, demand response systems, and grid stability management. Students gain experience in smart metering technologies and microgrid operations.
• Autonomous Vehicles & Robotics: Explores sensor fusion, path planning, control systems, and machine vision for autonomous vehicles. Students design and test robotic systems using Arduino and ROS platforms.
• Nanomaterials & Nanotechnology: Studies the synthesis, characterization, and applications of nanoscale materials. Labs involve scanning electron microscopy (SEM), atomic force microscopy (AFM), and nanofabrication techniques.
• Advanced Thermodynamics & Heat Transfer: Extends classical thermodynamic principles to include non-equilibrium processes and advanced heat transfer mechanisms. Applications include thermal design of electronic devices and energy systems.
• Cybersecurity and Ethical Hacking: Teaches defensive and offensive cybersecurity techniques, including penetration testing, vulnerability assessment, and incident response. Students work with industry-standard tools like Kali Linux and Metasploit.
• Industrial Automation & PLC Programming: Focuses on programmable logic controllers (PLCs), industrial communication protocols, and automation systems in manufacturing environments. Labs involve configuring and debugging PLC-based control systems.
• Advanced Signal Processing: Covers advanced signal processing techniques including wavelet transforms, adaptive filtering, and spectral estimation. Students apply these methods to audio and image processing applications.
• Financial Engineering & Risk Modeling: Integrates engineering principles with financial markets, focusing on derivative pricing, portfolio optimization, and risk management models. Students use Python and QuantLib for quantitative analysis.
• Materials Characterization Techniques: Provides in-depth knowledge of X-ray diffraction (XRD), electron microscopy, and spectroscopy methods. Students learn to interpret data from various characterization instruments used in materials research.
• Control Systems Design: Builds upon basic control theory to cover advanced control design techniques such as state-space representation, optimal control, and robust control. Students design controllers for complex systems using MATLAB/Simulink.
• Biomedical Instrumentation: Explores the design and application of medical devices including ECG monitors, MRI systems, and ultrasound equipment. Labs involve building prototype instrumentation with microcontrollers and sensors.

Project-Based Learning Philosophy

The department emphasizes project-based learning as a core component of its curriculum. This approach encourages students to apply theoretical knowledge in practical scenarios, develop problem-solving skills, and collaborate effectively in teams.

Mini-projects are introduced in the second year, requiring students to solve real-world problems using engineering principles. These projects are typically completed within 3-4 weeks and involve research, design, prototyping, and documentation phases.

The final-year thesis or capstone project provides a comprehensive platform for students to demonstrate their mastery of engineering concepts. Students select projects aligned with current industry trends and societal challenges, often involving collaboration with external partners such as startups or government agencies.

Project selection is facilitated through faculty mentorship, where students present project proposals based on available resources and expertise. Evaluation criteria include innovation, feasibility, technical depth, presentation quality, and teamwork performance. The department also organizes annual project showcases to celebrate student achievements and facilitate networking with industry professionals.