Comprehensive Course Structure
| Semester | Course Code | Course Title | Credit (L-T-P-C) | Prerequisites |
|---|---|---|---|---|
| I | MAT101 | Calculus and Analytical Geometry | 4-0-0-4 | - |
| I | PHY101 | Physics for Engineers | 4-0-0-4 | - |
| I | CHM101 | Chemistry for Engineering Students | 3-0-0-3 | - |
| I | ENG101 | English for Engineers | 2-0-0-2 | - |
| I | ECE101 | Introduction to Electrical Engineering | 3-0-0-3 | - |
| I | CSE101 | Computer Programming | 3-0-0-3 | - |
| I | MAT102 | Linear Algebra and Differential Equations | 4-0-0-4 | MAT101 |
| I | PHY102 | Physics Laboratory | 0-0-2-2 | PHY101 |
| I | CHM102 | Chemistry Laboratory | 0-0-2-2 | CHM101 |
| I | CSE102 | Data Structures and Algorithms | 3-0-0-3 | CSE101 |
| I | ECE102 | Electrical Circuits and Networks | 4-0-0-4 | ECE101 |
| I | ENG102 | Technical Communication | 2-0-0-2 | ENG101 |
| II | MAT201 | Probability and Statistics | 3-0-0-3 | MAT102 |
| II | PHY201 | Modern Physics | 3-0-0-3 | PHY102 |
| II | CHM201 | Organic Chemistry | 3-0-0-3 | CHM102 |
| II | ECE201 | Electronic Devices and Circuits | 4-0-0-4 | ECE102 |
| II | CSE201 | Object-Oriented Programming | 3-0-0-3 | CSE102 |
| II | MAT202 | Numerical Methods | 3-0-0-3 | MAT201 |
| II | PHY202 | Physics Laboratory II | 0-0-2-2 | PHY201 |
| II | CHM202 | Chemistry Laboratory II | 0-0-2-2 | CHM201 |
| II | CSE202 | Database Management Systems | 3-0-0-3 | CSE201 |
| II | ECE202 | Digital Electronics | 4-0-0-4 | ECE201 |
| III | MAT301 | Complex Analysis | 3-0-0-3 | MAT202 |
| III | PHY301 | Optics and Modern Physics | 3-0-0-3 | PHY202 |
| III | ECE301 | Signals and Systems | 4-0-0-4 | ECE202 |
| III | CSE301 | Computer Architecture | 3-0-0-3 | CSE202 |
| III | MAT302 | Linear Programming and Optimization | 3-0-0-3 | MAT301 |
| III | PHY302 | Physics Laboratory III | 0-0-2-2 | PHY301 |
| III | ECE302 | Control Systems | 4-0-0-4 | ECE301 |
| III | CSE302 | Operating Systems | 3-0-0-3 | CSE301 |
| III | MAT303 | Probability and Stochastic Processes | 3-0-0-3 | MAT302 |
| IV | ECE401 | Microprocessors and Microcontrollers | 4-0-0-4 | ECE302 |
| IV | CSE401 | Software Engineering | 3-0-0-3 | CSE302 |
| IV | MAT401 | Advanced Mathematics for Engineers | 3-0-0-3 | MAT303 |
| IV | ECE402 | Antennas and Wave Propagation | 4-0-0-4 | ECE401 |
| IV | CSE402 | Machine Learning Fundamentals | 3-0-0-3 | CSE401 |
| IV | MAT402 | Numerical Analysis and Scientific Computing | 3-0-0-3 | MAT401 |
| V | ECE501 | Embedded Systems Design | 4-0-0-4 | ECE402 |
| V | CSE501 | Cloud Computing and Big Data Analytics | 3-0-0-3 | CSE402 |
| V | ECE502 | RF and Microwave Engineering | 4-0-0-4 | ECE501 |
| V | CSE502 | Distributed Systems | 3-0-0-3 | CSE501 |
| V | ECE503 | Power Electronics and Drives | 4-0-0-4 | ECE502 |
| VI | ECE601 | Neural Networks and Deep Learning | 4-0-0-4 | ECE503 |
| VI | CSE601 | Cybersecurity and Ethical Hacking | 3-0-0-3 | CSE502 |
| VI | ECE602 | Advanced Digital Signal Processing | 4-0-0-4 | ECE601 |
| VI | CSE602 | DevOps and Containerization | 3-0-0-3 | CSE601 |
| VII | ECE701 | Robotics and Automation | 4-0-0-4 | ECE602 |
| VII | CSE701 | Blockchain Technologies | 3-0-0-3 | CSE602 |
| VIII | ECE801 | Capstone Project | 4-0-0-4 | ECE701 |
| VIII | CSE801 | Final Year Thesis | 4-0-0-4 | CSE701 |
Detailed Departmental Elective Courses
Advanced courses in departmental electives are designed to deepen students' understanding of specialized areas within engineering and prepare them for cutting-edge industry roles.
Neural Networks and Deep Learning
This course explores the theoretical foundations and practical applications of neural networks, including deep learning architectures such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers. Students learn to implement models using frameworks like TensorFlow and PyTorch, apply them to real-world datasets, and evaluate performance metrics.
Cybersecurity and Ethical Hacking
This course covers essential cybersecurity principles, including network security protocols, cryptography, intrusion detection systems, and ethical hacking techniques. Through hands-on labs and simulations, students gain practical experience in identifying vulnerabilities and defending against cyber threats.
Advanced Digital Signal Processing
Building on foundational knowledge of signal processing, this course delves into advanced topics such as multirate signal processing, adaptive filtering, spectral estimation, and wavelet transforms. Students implement algorithms using MATLAB and Python and apply them to audio, image, and biomedical signals.
DevOps and Containerization
This elective introduces students to DevOps practices and tools used in modern software development cycles. Topics include continuous integration/continuous deployment (CI/CD), containerization with Docker and Kubernetes, infrastructure as code, and automation pipelines for scalable applications.
Robotics and Automation
This course combines principles of mechanical engineering, electrical engineering, and computer science to explore robotics design, control systems, sensor integration, and autonomous navigation. Students build physical robots and program them using ROS (Robot Operating System).
Blockchain Technologies
This course explores the architecture, consensus mechanisms, smart contracts, and decentralized applications of blockchain technology. Students learn to develop blockchain-based solutions using Ethereum and Hyperledger frameworks and analyze real-world use cases across finance, supply chain, and healthcare.
Power Electronics and Drives
Focusing on power conversion and motor drives, this course covers rectifiers, inverters, DC-DC converters, and variable frequency drives. Students design and simulate power electronic circuits using simulation software and understand industrial applications in renewable energy and electric vehicles.
Embedded Systems Design
This course teaches students how to design embedded systems for IoT devices, microcontrollers, and real-time operating systems. Emphasis is placed on hardware-software co-design, programming in C/C++, real-time constraints, and integration with sensors and actuators.
Antennas and Wave Propagation
This course examines the theory and design of antennas for various communication systems, including dipole, patch, and array antennas. Students study wave propagation phenomena, radiation patterns, impedance matching techniques, and applications in wireless communications.
Cloud Computing and Big Data Analytics
Students learn about cloud platforms (AWS, Azure, GCP), distributed computing models, big data frameworks like Hadoop and Spark, and machine learning on scalable platforms. Projects involve analyzing large datasets using cloud resources and building predictive models.
Project-Based Learning Philosophy
The department's philosophy on project-based learning is centered around experiential education that bridges theory and practice. Students engage in mini-projects throughout their academic journey, culminating in a final-year capstone project or thesis.
Mini-projects are typically completed during the second and third years of study, with students working in teams to solve real-world problems under faculty supervision. These projects encourage innovation, teamwork, and practical application of learned concepts.
The final-year thesis or capstone project allows students to pursue independent research or design challenges aligned with their interests and career goals. Faculty mentors guide students through the research process, from problem identification to documentation and presentation.
Project selection involves a competitive process where students propose topics based on their academic interests and faculty availability. Evaluation criteria include originality, technical depth, feasibility, teamwork, and final deliverables. The department also encourages participation in national and international competitions and hackathons.