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Fees
₹9,20,000
Placement
94.5%
Avg Package
₹6,50,000
Highest Package
₹12,00,000
Fees
₹9,20,000
Placement
94.5%
Avg Package
₹6,50,000
Highest Package
₹12,00,000
Seats
600
Students
1,800
Seats
600
Students
1,800
The curriculum for the Engineering program at F S University Firozabad is meticulously designed to provide students with a robust foundation in core engineering principles while fostering innovation, creativity, and professional readiness. The structure spans eight semesters, each carefully curated to ensure progressive learning, real-world application, and industry alignment.
The following table presents a comprehensive overview of all courses offered across the eight semesters:
| Semester | Course Code | Course Title | Credits (L-T-P-C) | Prerequisites |
|---|---|---|---|---|
| 1 | ENG101 | Engineering Mathematics 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 | Engineering Graphics and Design | 2-0-2-4 | - |
| 1 | ENG105 | Computer Programming Concepts | 2-0-2-4 | - |
| 1 | ENG106 | Introduction to Engineering | 2-0-0-2 | - |
| 2 | ENG201 | Engineering Mathematics II | 3-1-0-4 | ENG101 |
| 2 | ENG202 | Electrical Circuits and Networks | 3-1-0-4 | - |
| 2 | ENG203 | Mechanics of Materials | 3-1-0-4 | - |
| 2 | ENG204 | Engineering Thermodynamics | 3-1-0-4 | - |
| 2 | ENG205 | Programming in C++ | 2-0-2-4 | ENG105 |
| 2 | ENG206 | Engineering Ethics and Professionalism | 2-0-0-2 | - |
| 3 | ENG301 | Signals and Systems | 3-1-0-4 | ENG201 |
| 3 | ENG302 | Digital Logic Design | 3-1-0-4 | - |
| 3 | ENG303 | Fluid Mechanics and Hydraulic Machines | 3-1-0-4 | ENG204 |
| 3 | ENG304 | Materials Science and Engineering | 3-1-0-4 | - |
| 3 | ENG305 | Data Structures and Algorithms | 3-1-0-4 | ENG205 |
| 3 | ENG306 | Engineering Economics and Cost Analysis | 2-0-0-2 | - |
| 4 | ENG401 | Control Systems | 3-1-0-4 | ENG301 |
| 4 | ENG402 | Electromagnetic Fields and Waves | 3-1-0-4 | - |
| 4 | ENG403 | Manufacturing Processes | 3-1-0-4 | - |
| 4 | ENG404 | Probability and Statistics for Engineers | 3-1-0-4 | ENG201 |
| 4 | ENG405 | Object-Oriented Programming in Java | 2-0-2-4 | ENG205 |
| 4 | ENG406 | Project Management and Entrepreneurship | 2-0-0-2 | - |
| 5 | ENG501 | Computer Architecture | 3-1-0-4 | ENG302 |
| 5 | ENG502 | Advanced Mathematics for Engineers | 3-1-0-4 | ENG201 |
| 5 | ENG503 | Structural Analysis | 3-1-0-4 | ENG203 |
| 5 | ENG504 | Heat Transfer and Mass Transfer | 3-1-0-4 | - |
| 5 | ENG505 | Software Engineering | 3-1-0-4 | ENG305 |
| 5 | ENG506 | Industrial Engineering and Operations Research | 2-0-0-2 | - |
| 6 | ENG601 | Advanced Control Systems | 3-1-0-4 | ENG401 |
| 6 | ENG602 | Machine Learning and AI | 3-1-0-4 | ENG404 |
| 6 | ENG603 | Advanced Materials and Nanotechnology | 3-1-0-4 | ENG304 |
| 6 | ENG604 | Renewable Energy Systems | 3-1-0-4 | - |
| 6 | ENG605 | Embedded Systems Design | 3-1-0-4 | ENG405 |
| 6 | ENG606 | Research Methodology and Ethics | 2-0-0-2 | - |
| 7 | ENG701 | Capstone Project I | 4-0-0-4 | ENG505 |
| 7 | ENG702 | Advanced Signal Processing | 3-1-0-4 | ENG301 |
| 7 | ENG703 | Power Electronics and Drives | 3-1-0-4 | - |
| 7 | ENG704 | Advanced Manufacturing Technologies | 3-1-0-4 | ENG403 |
| 7 | ENG705 | Process Control and Instrumentation | 3-1-0-4 | - |
| 7 | ENG706 | Environmental Engineering | 2-0-0-2 | - |
| 8 | ENG801 | Capstone Project II | 6-0-0-6 | ENG701 |
| 8 | ENG802 | Advanced Topics in Engineering | 3-1-0-4 | - |
| 8 | ENG803 | Project Management and Leadership | 2-0-0-2 | - |
| 8 | ENG804 | Industrial Internship | 0-0-0-4 | - |
| 8 | ENG805 | Engineering Innovation and Entrepreneurship | 2-0-0-2 | - |
The following are detailed descriptions of selected advanced departmental elective courses:
This course provides a comprehensive introduction to machine learning algorithms, including supervised learning, unsupervised learning, reinforcement learning, and deep learning. Students learn how to implement these techniques using Python libraries such as scikit-learn, TensorFlow, and PyTorch. The course emphasizes practical applications in image recognition, natural language processing, recommendation systems, and robotics.
This course explores advanced control system design methods including state-space representation, optimal control, nonlinear control, and robust control theory. Students learn to model complex systems and design controllers that ensure stability, performance, and robustness in real-world applications.
The course covers power semiconductor devices, power conversion circuits, motor drives, and renewable energy integration. It includes hands-on lab sessions involving the design and implementation of power electronic converters for industrial and residential applications.
This course introduces students to modern materials science including nanomaterials, composites, smart materials, and their applications in engineering systems. Students engage in research projects related to material characterization, synthesis techniques, and performance optimization.
The course focuses on solar, wind, hydroelectric, and geothermal energy technologies. Students study the principles of energy conversion, system design, grid integration, and policy frameworks supporting renewable energy adoption.
This course teaches students how to design embedded systems using microcontrollers, real-time operating systems, and hardware-software co-design techniques. Topics include device drivers, communication protocols, and debugging tools for embedded applications.
The course covers advanced signal processing techniques including wavelet transforms, adaptive filtering, spectral estimation, and digital filter design. Applications in audio processing, biomedical engineering, and telecommunications are emphasized.
This course introduces students to process control systems used in chemical plants, refineries, and manufacturing facilities. It includes topics such as feedback control, feedforward control, PID tuning, and process simulation using MATLAB/Simulink.
The course provides a foundation in research methodology, scientific writing, data analysis, and ethical considerations in engineering research. Students learn to design experiments, analyze data, and communicate findings effectively through presentations and publications.
This is a mandatory component where students gain hands-on experience in an industrial setting. They work on real projects under the supervision of industry mentors, applying their academic knowledge to solve practical problems and gaining exposure to corporate culture and professional practices.
The department's philosophy on project-based learning emphasizes student-centered, inquiry-driven education that promotes collaboration, critical thinking, and innovation. Mini-projects are assigned at regular intervals throughout each semester to reinforce concepts learned in lectures and labs. These projects typically involve small teams working under faculty supervision and culminate in presentations or reports.
The final-year thesis/capstone project is a significant undertaking where students select a topic aligned with their interests or industry needs. They work closely with a faculty advisor to develop a research proposal, conduct experiments or simulations, and produce a comprehensive report. Projects often result in patents, publications, or startup ventures, showcasing the university's commitment to fostering entrepreneurial thinking and innovation.
