Curriculum Overview
The Computer Engineering program at BAGULA MUKHI COLLEGE OF TECHNOLOGY is structured over eight semesters, with a blend of core subjects, departmental electives, science electives, and laboratory training. This comprehensive approach ensures that students gain both breadth and depth in their understanding of computing systems.
First Year
| Course Code | Course Title | Credits (L-T-P-C) | Prerequisites |
|---|---|---|---|
| CE101 | Engineering Mathematics I | 4-0-0-4 | - |
| CE102 | Physics for Computer Engineering | 3-0-0-3 | - |
| CE103 | Introduction to Programming | 2-0-2-4 | - |
| CE104 | Basic Electrical Engineering | 3-0-0-3 | - |
| CE105 | Engineering Graphics and Design | 2-0-2-4 | - |
| CE106 | Communication Skills | 2-0-0-2 | - |
Second Year
| Course Code | Course Title | Credits (L-T-P-C) | Prerequisites |
|---|---|---|---|
| CE201 | Engineering Mathematics II | 4-0-0-4 | CE101 |
| CE202 | Digital Logic Design | 3-0-0-3 | CE104 |
| CE203 | Data Structures and Algorithms | 3-0-0-3 | CE103 |
| CE204 | Computer Organization | 3-0-0-3 | CE202 |
| CE205 | Electronics Circuits | 3-0-0-3 | CE104 |
| CE206 | Software Engineering Principles | 3-0-0-3 | CE103 |
Third Year
| Course Code | Course Title | Credits (L-T-T-C) | Prerequisites |
|---|---|---|---|
| CE301 | Operating Systems | 3-0-0-3 | CE204, CE203 |
| CE302 | Database Management Systems | 3-0-0-3 | CE203 |
| CE303 | Computer Networks | 3-0-0-3 | CE204 |
| CE304 | Compiler Design | 3-0-0-3 | CE203, CE202 |
| CE305 | Embedded Systems | 3-0-0-3 | CE204, CE205 |
| CE306 | Microprocessors and Microcontrollers | 3-0-0-3 | CE202 |
Fourth Year
| Course Code | Course Title | Credits (L-T-P-C) | Prerequisites |
|---|---|---|---|
| CE401 | Advanced Computer Architecture | 3-0-0-3 | CE204, CE306 |
| CE402 | Artificial Intelligence and Machine Learning | 3-0-0-3 | CE301, CE302 |
| CE403 | Cybersecurity Fundamentals | 3-0-0-3 | CE303 |
| CE404 | Software Project Management | 3-0-0-3 | CE206 |
| CE405 | Distributed Systems | 3-0-0-3 | CE303, CE301 |
| CE406 | Human-Computer Interaction | 3-0-0-3 | CE206 |
Fifth Year
| Course Code | Course Title | Credits (L-T-P-C) | Prerequisites |
|---|---|---|---|
| CE501 | Advanced Machine Learning | 3-0-0-3 | CE402 |
| CE502 | Cloud Computing and Big Data Analytics | 3-0-0-3 | CE401, CE302 |
| CE503 | Internet of Things (IoT) | 3-0-0-3 | CE305 |
| CE504 | Mobile Application Development | 3-0-0-3 | CE206 |
| CE505 | Computer Vision and Image Processing | 3-0-0-3 | CE402 |
| CE506 | Quantitative Finance for Computing | 3-0-0-3 | CE302, CE301 |
Sixth Year
| Course Code | Course Title | Credits (L-T-P-C) | Prerequisites |
|---|---|---|---|
| CE601 | Research Methodology | 2-0-0-2 | - |
| CE602 | Thesis Proposal | 2-0-0-2 | CE501 |
| CE603 | Advanced Topics in Computer Engineering | 3-0-0-3 | - |
| CE604 | Capstone Project I | 4-0-0-4 | CE501, CE502 |
| CE605 | Capstone Project II | 4-0-0-4 | CE604 |
| CE606 | Entrepreneurship and Innovation | 2-0-0-2 | - |
Departmental Elective Courses
Advanced departmental elective courses provide students with specialized knowledge in specific areas of interest:
- Deep Learning and Neural Networks: This course explores the theory and practice of deep learning architectures, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers. Students will implement models using frameworks like TensorFlow and PyTorch.
- Blockchain Technology: Focused on understanding distributed ledger technology, smart contracts, and cryptographic protocols. The course covers practical applications in finance, supply chain management, and digital identity verification.
- Reinforcement Learning: Students learn about decision-making processes under uncertainty using reinforcement learning algorithms. Applications include robotics, game playing, and autonomous systems.
- Computer Vision: Explores techniques for image processing, object detection, segmentation, and recognition. Practical implementation includes face recognition, augmented reality, and medical imaging.
- Natural Language Processing: Covers text analysis, language modeling, sentiment analysis, and machine translation. Students will build applications such as chatbots, summarization tools, and information extraction systems.
- Cybersecurity Management: Focuses on risk assessment, incident response, compliance frameworks, and security architecture. The course includes hands-on labs in penetration testing and secure coding practices.
- Internet of Things (IoT) Security: Examines vulnerabilities in IoT ecosystems and develops strategies for securing connected devices. Topics include wireless communication protocols, privacy concerns, and regulatory requirements.
- Quantum Computing Fundamentals: Introduces quantum algorithms, quantum circuits, and quantum error correction. Students will simulate quantum operations using Qiskit and explore potential applications in cryptography and optimization.
- Mobile Application Security: Covers security threats specific to mobile platforms, including malware analysis, secure coding practices, and privacy protection mechanisms.
- Big Data Engineering: Focuses on scalable data processing using tools like Apache Spark, Hadoop, and Kafka. Students will design systems for handling large datasets efficiently.
Project-Based Learning Philosophy
The department emphasizes project-based learning as a core component of the curriculum. Students engage in both mini-projects during their second year and a final-year thesis or capstone project that integrates all aspects of their learning.
Mini-projects are designed to reinforce concepts learned in class through practical application. These projects typically span one semester and involve small teams working on specific challenges related to course content. Evaluation criteria include technical execution, creativity, documentation quality, and presentation skills.
The final-year thesis/capstone project is a significant undertaking that allows students to explore advanced topics in depth. Students select their projects based on personal interests, faculty expertise, or industry requirements. They work closely with mentors throughout the process, receiving guidance on research methodologies, experimental design, and professional writing.
Project selection involves a formal proposal submission process where students present their ideas, objectives, and expected outcomes. Faculty advisors evaluate proposals based on feasibility, innovation, relevance to current trends, and alignment with departmental resources.
Throughout the project lifecycle, students receive regular feedback from mentors and peers, ensuring continuous improvement and professional development. The final deliverables include a detailed report, presentation slides, and demonstration of working software or hardware components.