Comprehensive Course Structure
| Semester | Course Code | Course Title | Credit (L-T-P-C) | Prerequisites |
|---|---|---|---|---|
| 1 | PHYS101 | Physics for Engineers | 3-1-0-4 | - |
| 1 | MATH101 | Engineering Mathematics I | 3-1-0-4 | - |
| 1 | CE101 | Introduction to Computer Engineering | 2-0-0-2 | - |
| 1 | CS101 | Programming in C | 2-0-2-4 | - |
| 1 | PHYS102 | Physics Laboratory | 0-0-2-2 | - |
| 1 | MATH102 | Engineering Mathematics II | 3-1-0-4 | MATH101 |
| 2 | CS201 | Data Structures and Algorithms | 3-1-0-4 | CS101 |
| 2 | EC201 | Digital Logic Design | 3-1-0-4 | - |
| 2 | CS202 | Object Oriented Programming with Java | 2-0-2-4 | CS101 |
| 2 | MATH201 | Statistics and Probability | 3-1-0-4 | MATH102 |
| 2 | PHYS201 | Electrical Circuits | 3-1-0-4 | PHYS101 |
| 3 | CS301 | Computer Architecture | 3-1-0-4 | EC201 |
| 3 | CS302 | Operating Systems | 3-1-0-4 | CS201 |
| 3 | CS303 | Database Management Systems | 3-1-0-4 | CS201 |
| 3 | EC301 | Signals and Systems | 3-1-0-4 | MATH201 |
| 3 | CS304 | Software Engineering | 3-1-0-4 | CS202 |
| 4 | CS401 | Microprocessor Architecture | 3-1-0-4 | CS301 |
| 4 | CS402 | Computer Networks | 3-1-0-4 | EC301 |
| 4 | CS403 | Control Systems | 3-1-0-4 | MATH201 |
| 4 | CS404 | Embedded Systems Design | 3-1-0-4 | EC201 |
| 5 | CS501 | Artificial Intelligence and Machine Learning | 3-1-0-4 | CS301 |
| 5 | CS502 | Cybersecurity | 3-1-0-4 | CS402 |
| 5 | CS503 | Data Analytics and Visualization | 3-1-0-4 | CS201 |
| 5 | CS504 | Network Security | 3-1-0-4 | CS402 |
| 6 | CS601 | Advanced Embedded Systems | 3-1-0-4 | CS404 |
| 6 | CS602 | Deep Learning and Neural Networks | 3-1-0-4 | CS501 |
| 6 | CS603 | Software Testing and Quality Assurance | 3-1-0-4 | CS304 |
| 6 | CS604 | Cloud Computing | 3-1-0-4 | CS402 |
| 7 | CS701 | Robotics and Automation | 3-1-0-4 | CS404 |
| 7 | CS702 | Internet of Things (IoT) | 3-1-0-4 | CS402 |
| 7 | CS703 | Human-Computer Interaction | 3-1-0-4 | CS304 |
| 7 | CS704 | Machine Learning for Robotics | 3-1-0-4 | CS501 |
| 8 | CS801 | Final Year Project/Thesis | 6-0-0-6 | CS701 |
| 8 | CS802 | Internship | 3-0-0-3 | - |
Advanced Departmental Electives
The department offers a variety of advanced elective courses tailored to meet the evolving demands of industry and research. These courses are designed to provide students with specialized knowledge and skills in emerging areas of computer engineering.
Artificial Intelligence and Machine Learning: This course delves into the fundamentals of artificial intelligence, covering topics such as neural networks, deep learning architectures, natural language processing, and computer vision. Students learn to apply these concepts to real-world problems through practical projects involving data mining, predictive modeling, and intelligent system design.
Cybersecurity: The course focuses on protecting digital assets and infrastructure from cyber threats. It covers cryptography, network security, ethical hacking, and risk management. Through hands-on labs and simulations, students gain practical experience in identifying vulnerabilities and implementing robust defense mechanisms.
Data Analytics and Visualization: This elective introduces students to statistical modeling, data mining techniques, and visualization tools. Students learn to extract meaningful insights from large datasets using Python, R, and SQL. The course emphasizes real-world applications in business intelligence, healthcare analytics, and financial forecasting.
Network Security: The course explores the principles and practices of securing computer networks. Topics include firewalls, intrusion detection systems, secure protocols, and network auditing. Students engage in practical exercises to simulate attacks and defend against them using advanced tools and methodologies.
Advanced Embedded Systems: This course focuses on designing and developing embedded systems with advanced functionalities. Students study microcontroller programming, real-time operating systems, sensor integration, and system-on-chip (SoC) design. The course includes building prototypes for various applications including automotive, medical devices, and smart home systems.
Deep Learning and Neural Networks: This course provides in-depth knowledge of deep learning architectures and neural network models. Students learn to build and train complex models using frameworks like TensorFlow and PyTorch. The course covers image recognition, natural language processing, and reinforcement learning techniques.
Software Testing and Quality Assurance: This elective covers the principles and practices of software testing and quality assurance. Students learn about test planning, execution, automation tools, and defect tracking. The course includes hands-on experience with industry-standard tools like Selenium, JUnit, and TestNG.
Cloud Computing: The course introduces students to cloud computing concepts, including virtualization, distributed systems, and service models (IaaS, PaaS, SaaS). Students gain practical experience in deploying applications on platforms like AWS, Azure, and Google Cloud.
Robotics and Automation: This course integrates mechanical, electrical, and computer engineering to create autonomous machines. Students study robot design, sensor integration, control algorithms, and machine learning applications in robotics. The course includes building robots that can perform tasks such as navigation, manipulation, and interaction with humans.
Internet of Things (IoT): The course explores the architecture and protocols of IoT systems. Students learn to design and implement sensor networks, connect devices using wireless communication, and develop applications for smart environments. Practical sessions involve working with platforms like Arduino, Raspberry Pi, and Node-RED.
Human-Computer Interaction: This course focuses on designing interfaces that are intuitive, efficient, and user-friendly. Students study cognitive psychology, usability testing, interface prototyping, and user experience design principles. The course includes hands-on projects where students create interactive systems for various domains including healthcare, education, and entertainment.
Project-Based Learning Philosophy
The department's philosophy on project-based learning is rooted in the belief that students learn best when they apply theoretical knowledge to solve real-world problems. This approach fosters creativity, innovation, and teamwork while developing practical skills essential for professional success.
Mini-projects are assigned throughout the program starting from the second year. These projects allow students to explore specific topics within their chosen area of interest under faculty supervision. The projects typically involve small teams of 3-5 students who collaborate to design, implement, and present solutions to given problems.
The final-year project or thesis is a comprehensive endeavor that requires students to integrate knowledge from all previous courses. Students select a topic in consultation with faculty members and work on it for the entire academic year. The project involves extensive research, experimentation, documentation, and presentation of findings.
Evaluation criteria include technical competency, creativity, teamwork, presentation skills, and adherence to deadlines. Students are assessed through peer reviews, faculty evaluations, and final presentations. This rigorous evaluation process ensures that students develop not only technical expertise but also communication and leadership abilities.