Comprehensive Course Structure
The Computer Engineering curriculum at NAGAJI INSTITUTE OF TECHNOLOGY AND MANAGEMENT GWALIOR is structured over eight semesters to ensure a progressive and well-rounded education. Below is a detailed breakdown of the courses offered:
| Semester | Course Code | Course Title | Credit Structure (L-T-P-C) | Prerequisites |
|---|---|---|---|---|
| I | CE101 | Engineering Mathematics I | 3-1-0-4 | None |
| I | CE102 | Physics for Engineers | 3-1-0-4 | None |
| I | CE103 | Basic Electrical Engineering | 3-1-0-4 | None |
| I | CE104 | Introduction to Programming | 3-1-0-4 | None |
| I | CE105 | Computer Fundamentals | 2-1-0-3 | None |
| I | CE106 | Engineering Drawing | 1-0-2-2 | None |
| I | CE107 | Communication Skills | 2-0-0-2 | None |
| II | CE201 | Engineering Mathematics II | 3-1-0-4 | CE101 |
| II | CE202 | Chemistry for Engineers | 3-1-0-4 | None |
| II | CE203 | Digital Electronics | 3-1-0-4 | CE103 |
| II | CE204 | Data Structures and Algorithms | 3-1-0-4 | CE104 |
| II | CE205 | Object-Oriented Programming with C++ | 3-1-0-4 | CE104 |
| II | CE206 | Electronics Devices and Circuits | 3-1-0-4 | CE103 |
| III | CE301 | Probability and Statistics | 3-1-0-4 | CE201 |
| III | CE302 | Signals and Systems | 3-1-0-4 | CE201 |
| III | CE303 | Microprocessor and Microcontroller | 3-1-0-4 | CE203 |
| III | CE304 | Database Management Systems | 3-1-0-4 | CE204 |
| III | CE305 | Computer Organization and Architecture | 3-1-0-4 | CE203 |
| III | CE306 | Operating Systems | 3-1-0-4 | CE204 |
| IV | CE401 | Control Systems | 3-1-0-4 | CE302 |
| IV | CE402 | Software Engineering | 3-1-0-4 | CE204 |
| IV | CE403 | Networks and Protocols | 3-1-0-4 | CE305 |
| IV | CE404 | Compiler Design | 3-1-0-4 | CE204 |
| IV | CE405 | Computer Graphics | 3-1-0-4 | CE204 |
| IV | CE406 | Digital Signal Processing | 3-1-0-4 | CE302 |
| V | CE501 | Artificial Intelligence | 3-1-0-4 | CE304 |
| V | CE502 | Cybersecurity and Cryptography | 3-1-0-4 | CE304 |
| V | CE503 | Embedded Systems | 3-1-0-4 | CE303 |
| V | CE504 | Machine Learning | 3-1-0-4 | CE301 |
| V | CE505 | Data Mining and Analytics | 3-1-0-4 | CE301 |
| V | CE506 | Human-Computer Interaction | 3-1-0-4 | CE204 |
| VI | CE601 | Advanced Computer Architecture | 3-1-0-4 | CE305 |
| VI | CE602 | Distributed Systems | 3-1-0-4 | CE403 |
| VI | CE603 | Internet of Things | 3-1-0-4 | CE303 |
| VI | CE604 | Cloud Computing | 3-1-0-4 | CE402 |
| VI | CE605 | Mobile Computing | 3-1-0-4 | CE403 |
| VI | CE606 | Software Testing and Quality Assurance | 3-1-0-4 | CE402 |
| VII | CE701 | Research Methodology | 2-0-0-2 | None |
| VII | CE702 | Capstone Project I | 4-0-0-4 | CE501, CE503 |
| VII | CE703 | Advanced Topics in AI | 3-1-0-4 | CE501 |
| VII | CE704 | Advanced Network Security | 3-1-0-4 | CE502 |
| VII | CE705 | Robotics and Automation | 3-1-0-4 | CE503 |
| VIII | CE801 | Capstone Project II | 6-0-0-6 | CE702 |
| VIII | CE802 | Internship | 4-0-0-4 | CE501, CE503 |
Detailed Departmental Elective Courses
The department offers several advanced elective courses that allow students to explore specialized areas of interest:
- Advanced Machine Learning: This course covers deep learning architectures, reinforcement learning, and natural language processing techniques. Students learn to implement complex models using TensorFlow and PyTorch.
- Blockchain Technologies: Focuses on cryptographic principles, smart contracts, distributed ledgers, and decentralized applications. Students build real-world blockchain solutions for supply chain management and financial services.
- Quantum Computing: Introduces quantum algorithms, quantum gates, superposition, entanglement, and error correction. The course includes hands-on labs with IBM Q Experience platform.
- Computer Vision: Explores image processing, feature extraction, object detection, and neural network architectures for visual recognition tasks. Students develop applications using OpenCV and deep learning frameworks.
- Natural Language Processing: Covers text preprocessing, sentiment analysis, language modeling, and transformer architectures. Students implement chatbots and machine translation systems.
- DevOps Practices: Teaches continuous integration, containerization with Docker, orchestration with Kubernetes, and automation tools like Jenkins. Students deploy applications in cloud environments.
- Embedded Systems Design: Focuses on real-time operating systems, hardware-software co-design, microcontroller programming, and sensor integration. Projects include developing wearable devices and home automation systems.
- Cybersecurity Research: Involves ethical hacking, penetration testing, incident response, and security frameworks like NIST CSF. Students conduct vulnerability assessments and develop security policies.
- Mobile Application Development: Covers Android and iOS platforms, mobile UI/UX design, API integration, and app deployment strategies. Students build full-stack mobile apps with backend services.
- Human-Computer Interaction: Studies cognitive psychology, usability testing, prototyping, and user experience design. Students create interactive interfaces for diverse user groups.
Project-Based Learning Philosophy
The department strongly believes in project-based learning as a means to bridge the gap between theory and practice. Projects are structured to simulate real-world scenarios, encouraging students to apply their knowledge creatively and collaboratively.
Mini-projects begin in the third semester and continue through the sixth semester. These projects typically last 6-8 weeks and involve teams of 3-5 students working under faculty supervision. The scope ranges from simple implementation tasks to complex system designs.
The final-year thesis/capstone project is a comprehensive endeavor that spans the entire eighth semester. Students work closely with faculty mentors to select a topic aligned with current industry trends or personal interests. The evaluation criteria include technical depth, innovation, presentation quality, and impact on real-world problems.
Project selection is facilitated through a mentorship program where students are matched with faculty members based on their interests and research areas. Regular progress reviews ensure that projects stay on track and meet academic standards.