Course Structure Overview
| Semester | Course Code | Course Title | Credit (L-T-P-C) | Prerequisites |
|---|---|---|---|---|
| 1 | ENG101 | English for Engineering Communication | 3-0-0-3 | - |
| 1 | MAT101 | Calculus I | 4-0-0-4 | - |
| 1 | MAT102 | Linear Algebra and Differential Equations | 3-0-0-3 | MAT101 |
| 1 | PHY101 | Physics I | 4-0-0-4 | - |
| 1 | CHM101 | Chemistry for Engineers | 3-0-0-3 | - |
| 1 | CSE101 | Introduction to Programming | 3-0-0-3 | - |
| 1 | EEE101 | Basic Electrical Engineering | 3-0-0-3 | - |
| 2 | MAT201 | Calculus II | 4-0-0-4 | MAT102 |
| 2 | MAT202 | Probability and Statistics | 3-0-0-3 | MAT102 |
| 2 | PHY201 | Physics II | 4-0-0-4 | PHY101 |
| 2 | CSE201 | Data Structures and Algorithms | 3-0-0-3 | CSE101 |
| 2 | EEE201 | Circuit Analysis | 3-0-0-3 | EEE101 |
| 2 | EEE202 | Digital Logic Design | 3-0-0-3 | EEE101 |
| 3 | MAT301 | Advanced Calculus | 4-0-0-4 | MAT201 |
| 3 | EEE301 | Electromagnetic Fields and Waves | 3-0-0-3 | PHY201, MAT201 |
| 3 | EEE302 | Signals and Systems | 3-0-0-3 | EEE201 |
| 3 | EEE303 | Electrical Machines I | 3-0-0-3 | EEE201 |
| 4 | EEE401 | Control Systems | 3-0-0-3 | EEE302 |
| 4 | EEE402 | Power System Analysis | 3-0-0-3 | EEE303 |
| 4 | EEE403 | Digital Signal Processing | 3-0-0-3 | EEE302 |
| 5 | EEE501 | Microprocessors and Microcontrollers | 3-0-0-3 | CSE201 |
| 5 | EEE502 | Electronics Devices and Circuits | 3-0-0-3 | EEE301 |
| 5 | EEE503 | Communication Systems | 3-0-0-3 | EEE302 |
| 6 | EEE601 | Power Electronics | 3-0-0-3 | EEE303 |
| 6 | EEE602 | Renewable Energy Systems | 3-0-0-3 | EEE402 |
| 6 | EEE603 | Embedded Systems Design | 3-0-0-3 | EEE501 |
| 7 | EEE701 | Artificial Intelligence and Machine Learning | 3-0-0-3 | EEE302 |
| 7 | EEE702 | VLSI Design | 3-0-0-3 | EEE502 |
| 7 | EEE703 | Biomedical Engineering | 3-0-0-3 | EEE302 |
| 8 | EEE801 | Capstone Project | 4-0-0-4 | All previous courses |
| 8 | EEE802 | Industry Internship | 4-0-0-4 | EEE801 |
Advanced Departmental Electives
Renewable Energy Systems: This course focuses on solar, wind, hydroelectric, and other sustainable energy technologies. Students learn about energy conversion systems, grid integration, and environmental impact assessment.
Embedded Systems Design: Students explore microcontroller architectures, real-time operating systems, sensor integration, and design of embedded applications for smart devices.
Power Electronics: Covers power converters, inverters, rectifiers, and their applications in industrial and consumer electronics. Emphasizes practical implementation using simulation tools like MATLAB/Simulink.
Artificial Intelligence and Machine Learning: Integrates AI concepts with electrical engineering practices. Topics include neural networks, deep learning frameworks, and AI in embedded systems.
VLSI Design: Teaches integrated circuit design principles, layout design, CAD tools, and system-on-chip architecture using industry-standard software like Cadence and Synopsys.
Biomedical Engineering: Combines electrical engineering principles with medical applications. Students study biomedical instrumentation, health informatics, and medical imaging techniques.
Control Systems: Focuses on automatic control theory, feedback systems, stability analysis, and design of controllers for industrial processes.
Digital Signal Processing: Covers discrete-time signals, sampling theorem, Z-transforms, FFT algorithms, and application in audio/video processing.
Communication Systems: Explores analog and digital modulation techniques, channel coding, wireless communication protocols, and network security.
Microprocessors and Microcontrollers: Involves architecture, instruction set, assembly language programming, and interfacing with peripheral devices using embedded development boards.
Project-Based Learning Philosophy
The department emphasizes project-based learning as a core component of the curriculum. From the second year onwards, students engage in mini-projects that combine theoretical knowledge with practical implementation. These projects are designed to foster creativity, teamwork, and problem-solving skills.
Mini-projects are assigned based on student interest and faculty expertise. Each team consists of 3-5 students who work under the supervision of a faculty mentor. Projects are evaluated using rubrics that assess technical competence, innovation, presentation, and documentation.
The final-year capstone project is a significant undertaking that spans the entire semester. Students select a topic relevant to their specialization, conduct independent research, develop prototypes, and present findings to a panel of experts. This experience mirrors real-world engineering challenges and prepares students for industry or graduate school.