Comprehensive Course Structure
The Engineering curriculum at Plaksha University Mohali is meticulously structured to provide students with a holistic and progressive educational experience. The program spans eight semesters, integrating foundational knowledge with specialized expertise across multiple engineering domains.
| Semester | Course Code | Course Title | Credit Structure (L-T-P-C) | Pre-requisites |
|---|---|---|---|---|
| 1 | ENG101 | Calculus I | 3-1-0-4 | - |
| 1 | ENG102 | Physics for Engineers | 3-1-0-4 | - |
| 1 | ENG103 | Chemistry for Engineers | 3-1-0-4 | - |
| 1 | ENG104 | Introduction to Programming | 2-1-2-5 | - |
| 1 | ENG105 | Engineering Graphics | 2-1-2-5 | - |
| 1 | ENG106 | English for Engineers | 2-0-0-3 | - |
| 2 | ENG201 | Calculus II | 3-1-0-4 | ENG101 |
| 2 | ENG202 | Electrical Circuits | 3-1-0-4 | ENG102 |
| 2 | ENG203 | Digital Logic Design | 3-1-0-4 | - |
| 2 | ENG204 | Data Structures and Algorithms | 3-1-0-4 | ENG104 |
| 2 | ENG205 | Signals and Systems | 3-1-0-4 | ENG101 |
| 2 | ENG206 | Engineering Mechanics | 3-1-0-4 | ENG102 |
| 3 | ENG301 | Thermodynamics | 3-1-0-4 | ENG201 |
| 3 | ENG302 | Fluid Mechanics | 3-1-0-4 | ENG201 |
| 3 | ENG303 | Control Systems | 3-1-0-4 | ENG205 |
| 3 | ENG304 | Computer Architecture | 3-1-0-4 | ENG203 |
| 3 | ENG305 | Probability and Statistics | 3-1-0-4 | ENG101 |
| 3 | ENG306 | Signals and Systems Lab | 0-0-2-2 | ENG205 |
| 4 | ENG401 | Machine Learning | 3-1-0-4 | ENG305 |
| 4 | ENG402 | Database Systems | 3-1-0-4 | ENG204 |
| 4 | ENG403 | Embedded Systems | 3-1-0-4 | ENG203 |
| 4 | ENG404 | Advanced Mathematics | 3-1-0-4 | ENG201 |
| 4 | ENG405 | Industrial Engineering | 3-1-0-4 | ENG301 |
| 4 | ENG406 | Software Engineering Lab | 0-0-2-2 | ENG204 |
| 5 | ENG501 | Deep Learning | 3-1-0-4 | ENG401 |
| 5 | ENG502 | Cybersecurity Fundamentals | 3-1-0-4 | ENG204 |
| 5 | ENG503 | Advanced Control Systems | 3-1-0-4 | ENG303 |
| 5 | ENG504 | Operations Research | 3-1-0-4 | ENG305 |
| 5 | ENG505 | Project Management | 3-1-0-4 | - |
| 5 | ENG506 | Research Methodology Lab | 0-0-2-2 | ENG305 |
| 6 | ENG601 | Computer Vision | 3-1-0-4 | ENG401 |
| 6 | ENG602 | Network Security | 3-1-0-4 | ENG502 |
| 6 | ENG603 | Renewable Energy Systems | 3-1-0-4 | ENG301 |
| 6 | ENG604 | Advanced Robotics | 3-1-0-4 | ENG303 |
| 6 | ENG605 | Biomedical Signal Processing | 3-1-0-4 | ENG205 |
| 6 | ENG606 | Capstone Project Lab | 0-0-4-8 | ENG501 |
| 7 | ENG701 | Advanced Machine Learning | 3-1-0-4 | ENG501 |
| 7 | ENG702 | Blockchain Technology | 3-1-0-4 | ENG204 |
| 7 | ENG703 | Smart Grids | 3-1-0-4 | ENG301 |
| 7 | ENG704 | Advanced Data Analytics | 3-1-0-4 | ENG501 |
| 7 | ENG705 | Human Factors Engineering | 3-1-0-4 | - |
| 7 | ENG706 | Advanced Capstone Project | 0-0-4-8 | ENG606 |
| 8 | ENG801 | Research Thesis | 0-0-0-12 | ENG706 |
Detailed Departmental Elective Courses
Advanced Machine Learning is a course that delves deep into neural network architectures, reinforcement learning, and generative models. Students learn to implement complex algorithms using Python and TensorFlow, gaining insights into cutting-edge AI research.
Cybersecurity Fundamentals introduces students to cryptographic techniques, secure protocols, and threat analysis. It covers topics such as penetration testing, vulnerability assessment, and incident response planning.
Renewable Energy Systems explores solar, wind, hydroelectric, and geothermal energy technologies. Students study system design, efficiency optimization, and environmental impact assessments.
Advanced Robotics combines mechanical engineering principles with AI to develop intelligent robotic systems capable of autonomous navigation and task execution.
Biomedical Signal Processing focuses on analyzing physiological signals like ECG, EEG, and EMG using advanced signal processing techniques. Students gain hands-on experience in designing diagnostic tools and medical devices.
Computer Vision is centered around image recognition, object detection, and scene understanding. The course uses frameworks like OpenCV and PyTorch to build real-world applications.
Network Security examines secure network design, firewalls, intrusion detection systems, and policy enforcement mechanisms. Students learn how to protect enterprise networks from evolving threats.
Smart Grids explores the integration of renewable energy sources into existing power grids. Topics include grid stability, load forecasting, and smart metering technologies.
Advanced Data Analytics teaches students how to extract actionable insights from large datasets using statistical modeling and machine learning techniques.
Human Factors Engineering focuses on designing systems that are intuitive, safe, and user-friendly. It covers ergonomics, cognitive psychology, and usability testing methodologies.
Project-Based Learning Philosophy
The department emphasizes project-based learning as a core component of the curriculum. Students engage in both mini-projects during the second year and a final-year thesis or capstone project that spans multiple semesters.
Mini-projects are designed to reinforce classroom concepts through practical implementation. Each project is assigned based on student interest and faculty expertise, with mentorship provided throughout the process.
The final-year thesis or capstone project allows students to apply their knowledge to a real-world problem in collaboration with industry partners or research institutions. Students work under the guidance of faculty mentors who help them refine ideas, conduct experiments, and present findings at conferences or in academic journals.