Course Structure Overview
The Computer Engineering program at Institute of Engineering Jiwaji University Gwalior is structured over 8 semesters, with a comprehensive mix of core subjects, departmental electives, science electives, and practical laboratory sessions. The curriculum ensures a balanced progression from foundational concepts to advanced specializations.
| Semester | Course Code | Course Title | Credit Structure (L-T-P-C) | Prerequisites |
|---|---|---|---|---|
| 1 | CE101 | Engineering Mathematics I | 3-1-0-4 | - |
| 1 | CE102 | Physics for Engineers | 3-1-0-4 | - |
| 1 | CE103 | Chemistry for Engineers | 3-1-0-4 | - |
| 1 | CE104 | Introduction to Computer Programming | 2-0-2-3 | - |
| 1 | CE105 | Engineering Graphics and Design | 2-0-2-3 | - |
| 1 | CE106 | English for Engineers | 2-0-0-2 | - |
| 2 | CE201 | Engineering Mathematics II | 3-1-0-4 | CE101 |
| 2 | CE202 | Digital Logic Design | 3-1-0-4 | - |
| 2 | CE203 | Data Structures and Algorithms | 3-1-0-4 | CE104 |
| 2 | CE204 | Electrical Circuits and Electronics | 3-1-0-4 | - |
| 2 | CE205 | Computer Organization | 3-1-0-4 | CE202 |
| 2 | CE206 | Lab Session: Programming and Electronics | 0-0-4-2 | - |
| 3 | CE301 | Operating Systems | 3-1-0-4 | CE203, CE205 |
| 3 | CE302 | Computer Networks | 3-1-0-4 | CE204 |
| 3 | CE303 | Database Management Systems | 3-1-0-4 | CE203 |
| 3 | CE304 | Software Engineering | 3-1-0-4 | CE203 |
| 3 | CE305 | Signals and Systems | 3-1-0-4 | CE102, CE201 |
| 3 | CE306 | Lab Session: Operating Systems and Networks | 0-0-4-2 | - |
| 4 | CE401 | Microprocessor and Microcontroller | 3-1-0-4 | CE204, CE205 |
| 4 | CE402 | Artificial Intelligence and Machine Learning | 3-1-0-4 | CE301, CE303 |
| 4 | CE403 | Cybersecurity Fundamentals | 3-1-0-4 | CE302, CE303 |
| 4 | CE404 | Embedded Systems Design | 3-1-0-4 | CE401 |
| 4 | CE405 | Human Computer Interaction | 3-1-0-4 | CE203 |
| 4 | CE406 | Lab Session: Embedded Systems and AI | 0-0-4-2 | - |
| 5 | CE501 | Advanced Computer Architecture | 3-1-0-4 | CE205, CE401 |
| 5 | CE502 | Distributed Systems | 3-1-0-4 | CE301, CE302 |
| 5 | CE503 | Data Mining and Big Data Analytics | 3-1-0-4 | CE303 |
| 5 | CE504 | Mobile Application Development | 3-1-0-4 | CE304 |
| 5 | CE505 | Cloud Computing and Virtualization | 3-1-0-4 | CE302, CE301 |
| 5 | CE506 | Lab Session: Distributed Systems and Cloud | 0-0-4-2 | - |
| 6 | CE601 | Internet of Things (IoT) Technologies | 3-1-0-4 | CE404, CE505 |
| 6 | CE602 | Computer Vision and Image Processing | 3-1-0-4 | CE305 |
| 6 | CE603 | Network Security and Cryptography | 3-1-0-4 | CE302, CE403 |
| 6 | CE604 | Machine Learning for Industry Applications | 3-1-0-4 | CE402 |
| 6 | CE605 | Reinforcement Learning and Robotics | 3-1-0-4 | CE402 |
| 6 | CE606 | Lab Session: IoT, Vision, and RL | 0-0-4-2 | - |
| 7 | CE701 | Capstone Project I | 0-0-8-8 | CE501, CE502, CE503 |
| 7 | CE702 | Research Methodology and Ethics | 2-0-0-2 | - |
| 7 | CE703 | Special Topics in Computer Engineering | 3-1-0-4 | CE504, CE601 |
| 7 | CE704 | Entrepreneurship and Innovation | 2-0-0-2 | - |
| 7 | CE705 | Professional Skills Development | 2-0-0-2 | - |
| 7 | CE706 | Lab Session: Capstone Project | 0-0-4-2 | - |
| 8 | CE801 | Capstone Project II | 0-0-8-8 | CE701 |
| 8 | CE802 | Industry Internship | 0-0-4-4 | - |
| 8 | CE803 | Final Thesis Presentation | 0-0-2-2 | CE701, CE801 |
| 8 | CE804 | Graduation Ceremony Preparation | 0-0-2-2 | - |
Advanced Departmental Elective Courses
The department offers a wide array of advanced departmental electives designed to deepen students' knowledge and prepare them for specialized career paths. These courses are developed in alignment with current industry trends and research directions.
Computer Vision and Image Processing: This course introduces students to fundamental techniques in image analysis, pattern recognition, and computer vision algorithms. It covers topics such as edge detection, object tracking, facial recognition, and deep learning models for visual data processing. Students apply these concepts using libraries like OpenCV and TensorFlow.
Machine Learning for Industry Applications: This elective focuses on applying machine learning techniques in real-world scenarios across industries such as healthcare, finance, and manufacturing. Students learn to build predictive models, evaluate performance metrics, and deploy ML systems using frameworks like Scikit-learn and PyTorch.
Network Security and Cryptography: This course explores modern cryptographic algorithms, secure communication protocols, and defensive strategies against cyber threats. It includes hands-on labs on penetration testing, firewall configuration, and digital signature implementation.
Internet of Things (IoT) Technologies: Students gain in-depth knowledge of IoT architecture, sensor networks, wireless communication standards, and cloud integration. The course includes practical sessions on developing IoT applications using platforms like Arduino, Raspberry Pi, and AWS IoT Core.
Reinforcement Learning and Robotics: This advanced elective combines principles of reinforcement learning with robotics engineering. Students design and implement autonomous agents capable of learning optimal actions through trial-and-error interactions with environments.
Distributed Systems: This course delves into the design and implementation of distributed computing systems, covering topics such as consensus algorithms, fault tolerance, and scalability challenges in large-scale software architectures.
Cloud Computing and Virtualization: Students learn about cloud service models, virtual machine management, containerization technologies, and infrastructure-as-code practices. The course emphasizes real-world deployment scenarios using platforms like AWS, Azure, and Google Cloud Platform.
Data Mining and Big Data Analytics: This elective teaches students how to extract insights from large datasets using advanced analytical techniques and tools. Topics include clustering, classification, regression analysis, and graph theory applications in data science.
Mobile Application Development: Focused on building cross-platform mobile apps for iOS and Android, this course covers UI/UX design principles, API integration, user authentication, and app deployment strategies using Flutter and React Native frameworks.
Advanced Computer Architecture: This course explores cutting-edge developments in processor design, memory hierarchy optimization, parallel processing, and emerging architectures like quantum computing. It includes simulations and experiments with modern microprocessors.
Human-Computer Interaction (HCI): Students study the psychological and behavioral aspects of interaction between humans and computers, focusing on usability testing, accessibility standards, and user-centered design methodologies.
Embedded Systems Design: This elective provides comprehensive coverage of designing embedded systems for resource-constrained environments. It includes topics such as real-time operating systems, low-power design, and hardware-software co-design techniques.
Cybersecurity Fundamentals: Students learn core concepts in cybersecurity, including threat modeling, vulnerability assessment, incident response planning, and compliance frameworks. Practical exercises involve simulating attacks and defending against them using industry-standard tools.
Artificial Intelligence and Machine Learning: A foundational course covering supervised and unsupervised learning, neural networks, decision trees, support vector machines, and reinforcement learning. Emphasis is placed on practical implementation and real-world problem-solving using Python and popular ML libraries.
Microprocessor and Microcontroller: This course provides detailed insights into the architecture and operation of microprocessors and microcontrollers, including instruction set design, memory mapping, interrupt handling, and peripheral interfaces.
Software Engineering: Students gain hands-on experience in software development lifecycle, agile methodologies, version control systems, testing strategies, and project management tools used in modern software organizations.
Project-Based Learning Philosophy
The department strongly believes in experiential learning through project-based education. Projects are structured to encourage critical thinking, collaboration, and innovation while reinforcing theoretical knowledge gained during lectures.
Mini-projects begin in the third year, where students form teams of 3-5 members and work on small-scale applications or prototypes under faculty supervision. These projects typically last for one semester and involve problem identification, research, design, implementation, testing, and presentation phases.
The final-year thesis/capstone project is a significant component of the program. Students select topics aligned with their interests and career aspirations, working closely with faculty mentors throughout the process. Projects often result in publishable research papers or commercial products that are showcased at university events and industry conferences.
Faculty members play a pivotal role in guiding students through each stage of project development. They provide mentorship, feedback, and access to specialized resources including lab equipment, software licenses, and industry partnerships.
The evaluation criteria for projects emphasize not just technical execution but also creativity, presentation skills, teamwork, and adherence to deadlines. Students are assessed on their ability to define problems, propose solutions, implement designs, document findings, and communicate results effectively.