Comprehensive Course Structure
The Computer Engineering program at UJJAIN ENGINEERING COLLEGE FORMERLY GOVT ENGG COLLEGE is meticulously structured over eight semesters to provide a progressive and comprehensive educational experience. The curriculum integrates foundational sciences, core engineering principles, and specialized electives to ensure students develop both breadth and depth in their understanding of computer systems and technologies.
Semester-wise Course Distribution
| Semester | Course Code | Course Title | Credit Structure (L-T-P-C) | Prerequisites |
|---|---|---|---|---|
| I | CE101 | Engineering Mathematics I | 3-1-0-4 | - |
| I | CE102 | Physics for Engineers | 3-1-0-4 | - |
| I | CE103 | Chemistry for Engineers | 3-1-0-4 | - |
| I | CE104 | Introduction to Programming | 3-1-2-5 | - |
| I | CE105 | Basic Electrical Engineering | 3-1-0-4 | - |
| I | CE106 | Engineering Graphics & Design | 2-1-2-4 | - |
| I | CE107 | Workshop Practice | 0-0-2-2 | - |
| II | CE201 | Engineering Mathematics II | 3-1-0-4 | CE101 |
| II | CE202 | Digital Electronics | 3-1-0-4 | CE105 |
| II | CE203 | Data Structures & Algorithms | 3-1-2-5 | CE104 |
| II | CE204 | Object Oriented Programming with C++ | 3-1-2-5 | CE104 |
| II | CE205 | Electronics Circuits | 3-1-0-4 | CE105 |
| II | CE206 | Environmental Science & Engineering | 3-1-0-4 | - |
| III | CE301 | Computer Organization & Architecture | 3-1-0-4 | CE202 |
| III | CE302 | Operating Systems | 3-1-2-5 | CE203 |
| III | CE303 | Database Management Systems | 3-1-2-5 | CE203 |
| III | CE304 | Web Technologies | 3-1-2-5 | CE204 |
| III | CE305 | Digital Signal Processing | 3-1-0-4 | CE201 |
| III | CE306 | Microprocessors & Microcontrollers | 3-1-2-5 | CE202 |
| IV | CE401 | Software Engineering | 3-1-2-5 | CE303 |
| IV | CE402 | Computer Networks | 3-1-0-4 | CE301 |
| IV | CE403 | Artificial Intelligence | 3-1-2-5 | CE301 |
| IV | CE404 | Cybersecurity Fundamentals | 3-1-2-5 | CE301 |
| IV | CE405 | Embedded Systems | 3-1-2-5 | CE306 |
| IV | CE406 | Internet of Things | 3-1-2-5 | CE306 |
| V | CE501 | Machine Learning | 3-1-2-5 | CE403 |
| V | CE502 | Big Data Analytics | 3-1-2-5 | CE401 |
| V | CE503 | Cloud Computing | 3-1-2-5 | CE401 |
| V | CE504 | Robotics & Automation | 3-1-2-5 | CE405 |
| V | CE505 | Human-Computer Interaction | 3-1-2-5 | CE401 |
| V | CE506 | Advanced Computer Architecture | 3-1-0-4 | CE301 |
| VI | CE601 | Deep Learning | 3-1-2-5 | CE501 |
| VI | CE602 | Blockchain Technology | 3-1-2-5 | CE404 |
| VI | CE603 | Reinforcement Learning | 3-1-2-5 | CE501 |
| VI | CE604 | Mobile Application Development | 3-1-2-5 | CE401 |
| VI | CE605 | Signal Processing Applications | 3-1-0-4 | CE305 |
| VI | CE606 | Advanced Cybersecurity | 3-1-2-5 | CE404 |
| VII | CE701 | Research Methodology | 2-0-2-3 | - |
| VII | CE702 | Capstone Project I | 0-0-6-6 | - |
| VIII | CE801 | Capstone Project II | 0-0-6-6 | CE702 |
| VIII | CE802 | Internship | 0-0-12-12 | - |
Advanced Departmental Elective Courses
Advanced departmental electives are designed to deepen students' understanding of specialized areas within computer engineering. These courses provide exposure to emerging technologies and industry-relevant topics that align with current trends in the field.
Machine Learning
This course introduces students to fundamental concepts in machine learning, including supervised and unsupervised learning algorithms, neural networks, decision trees, clustering techniques, and reinforcement learning. Students gain hands-on experience through practical projects involving real-world datasets and industry applications.
Big Data Analytics
Focused on processing and analyzing large volumes of data, this course covers Hadoop, Spark, NoSQL databases, and statistical modeling techniques. Students learn to extract meaningful insights from big data sources using modern tools and frameworks.
Cloud Computing
Students explore cloud architecture, deployment models, service types, and management platforms such as AWS, Azure, and Google Cloud. The course includes practical sessions on deploying scalable applications in cloud environments.
Robotics & Automation
This elective integrates mechanical engineering principles with computer science to design autonomous systems. Students work with robotic kits, sensors, actuators, and control algorithms to build functional robots capable of performing complex tasks.
Human-Computer Interaction
Students study human factors in computing, usability testing, interface design, and accessibility standards. The course emphasizes the importance of user-centered design in creating effective software and hardware products.
Advanced Computer Architecture
This course delves into advanced topics such as cache memory design, pipelining, instruction set architecture (ISA), and parallel processing techniques. It prepares students for roles in hardware design and performance optimization.
Deep Learning
Focusing on neural network architectures like convolutional networks, recurrent networks, transformers, and generative adversarial networks, this course equips students with skills to develop advanced AI applications.
Blockchain Technology
Students explore blockchain fundamentals, smart contracts, consensus mechanisms, cryptographic protocols, and decentralized applications. The course includes practical implementation of blockchain solutions using platforms like Ethereum and Hyperledger.
Reinforcement Learning
This course covers theoretical foundations and practical implementations of reinforcement learning algorithms. Students learn to design agents that can learn optimal behaviors through interaction with environments.
Mobile Application Development
Students develop applications for iOS and Android platforms using modern frameworks like React Native, Flutter, and Swift/Kotlin. The course includes UI/UX design principles and app deployment strategies.
Signal Processing Applications
This elective explores the application of signal processing techniques in audio, image, and video analysis. Students work on projects involving speech recognition, computer vision, and multimedia systems.
Advanced Cybersecurity
Students study advanced security threats, penetration testing, malware analysis, network forensics, and incident response procedures. The course prepares graduates for roles in cybersecurity consulting and enterprise security management.
Project-Based Learning Philosophy
The department emphasizes a project-based learning approach throughout the curriculum to ensure students develop practical skills and real-world experience. Projects are structured to simulate industry challenges, encouraging creativity, teamwork, and innovation.
Mini-projects begin in the second year, allowing students to apply theoretical knowledge to small-scale problems. These projects typically last 4–6 weeks and involve individual or group work under faculty supervision. Students are evaluated based on project documentation, presentation skills, and technical execution.
The final-year thesis/capstone project is a significant component of the program, lasting 12–16 weeks. Students select topics related to their specialization or industry needs, working closely with assigned faculty mentors. Projects often result in patents, publications, or startup ventures, providing tangible proof of students' capabilities and potential impact.
Students choose their projects through a collaborative process involving faculty advisors, industry partners, and departmental reviews. The selection criteria consider student interest, academic performance, resource availability, and alignment with current technological trends. Faculty mentors provide continuous guidance throughout the project lifecycle, ensuring that students receive timely feedback and support.