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Fees
₹12,00,000
Placement
93.5%
Avg Package
₹6,50,000
Highest Package
₹9,50,000
Fees
₹12,00,000
Placement
93.5%
Avg Package
₹6,50,000
Highest Package
₹9,50,000
Seats
150
Students
800
Seats
150
Students
800
The engineering curriculum at Madhav University Sirohi is meticulously designed to provide a balanced blend of theoretical knowledge and practical application. The program spans eight semesters, with each semester carrying a total credit load of 20 credits, distributed across core courses, departmental electives, science electives, and laboratory sessions.
| Semester | Course Code | Course Title | Credit (L-T-P-C) | Pre-requisite |
|---|---|---|---|---|
| 1 | MTH101 | Calculus I | 3-1-0-4 | - |
| 1 | MTH102 | Linear Algebra | 3-1-0-4 | MTH101 |
| 1 | PHY101 | Physics I | 3-1-0-4 | - |
| 1 | CHM101 | Chemistry I | 3-1-0-4 | - |
| 1 | CSE101 | Introduction to Programming | 2-0-2-4 | - |
| 1 | ENG101 | English Communication | 2-0-0-2 | - |
| 1 | ECE101 | Basic Electronics | 3-1-0-4 | - |
| 1 | MEC101 | Mechanics of Materials | 3-1-0-4 | - |
| 1 | CIV101 | Building Materials | 3-1-0-4 | - |
| 2 | MTH201 | Calculus II | 3-1-0-4 | MTH102 |
| 2 | MTH202 | Differential Equations | 3-1-0-4 | MTH201 |
| 2 | PHY201 | Physics II | 3-1-0-4 | PHY101 |
| 2 | CHM201 | Chemistry II | 3-1-0-4 | CHM101 |
| 2 | CSE201 | Data Structures and Algorithms | 3-1-0-4 | CSE101 |
| 2 | ENG201 | Technical Writing | 2-0-0-2 | - |
| 2 | ECE201 | Circuit Analysis | 3-1-0-4 | ECE101 |
| 2 | MEC201 | Thermodynamics | 3-1-0-4 | MEC101 |
| 2 | CIV201 | Structural Analysis | 3-1-0-4 | CIV101 |
| 3 | MTH301 | Probability and Statistics | 3-1-0-4 | MTH202 |
| 3 | MTH302 | Complex Variables | 3-1-0-4 | MTH201 |
| 3 | PHY301 | Optics and Modern Physics | 3-1-0-4 | PHY201 |
| 3 | CSE301 | Database Management Systems | 3-1-0-4 | CSE201 |
| 3 | ECE301 | Signals and Systems | 3-1-0-4 | ECE201 |
| 3 | MEC301 | Fluid Mechanics | 3-1-0-4 | MEC201 |
| 3 | CIV301 | Geotechnical Engineering | 3-1-0-4 | CIV201 |
| 4 | MTH401 | Numerical Methods | 3-1-0-4 | MTH301 |
| 4 | CSE401 | Computer Networks | 3-1-0-4 | CSE301 |
| 4 | ECE401 | Digital Electronics | 3-1-0-4 | ECE301 |
| 4 | MEC401 | Heat Transfer | 3-1-0-4 | MEC301 |
| 4 | CIV401 | Transportation Engineering | 3-1-0-4 | CIV301 |
| 5 | CSE501 | Operating Systems | 3-1-0-4 | CSE401 |
| 5 | ECE501 | Control Systems | 3-1-0-4 | ECE401 |
| 5 | MEC501 | Mechanics of Machines | 3-1-0-4 | MEC401 |
| 5 | CIV501 | Water Resources Engineering | 3-1-0-4 | CIV401 |
| 6 | CSE601 | Machine Learning | 3-1-0-4 | CSE501 |
| 6 | ECE601 | Embedded Systems | 3-1-0-4 | ECE501 |
| 6 | MEC601 | Advanced Manufacturing | 3-1-0-4 | MEC501 |
| 6 | CIV601 | Environmental Engineering | 3-1-0-4 | CIV501 |
| 7 | CSE701 | Capstone Project | 2-0-6-8 | CSE601 |
| 7 | ECE701 | Research Project | 2-0-6-8 | ECE601 |
| 7 | MEC701 | Final Year Project | 2-0-6-8 | MEC601 |
| 7 | CIV701 | Infrastructure Development | 2-0-6-8 | CIV601 |
| 8 | CSE801 | Internship | 0-0-0-4 | - |
| 8 | ECE801 | Industry Collaboration | 0-0-0-4 | - |
| 8 | MEC801 | Professional Practice | 0-0-0-4 | - |
| 8 | CIV801 | Project Management | 0-0-0-4 | - |
Departmental electives offer students specialized knowledge in niche areas relevant to their career aspirations. These courses are designed to complement core engineering principles with advanced topics and emerging technologies.
The course 'Machine Learning' explores algorithms used in artificial intelligence, including supervised learning, unsupervised learning, neural networks, and reinforcement learning. Students develop practical skills through hands-on projects using Python libraries like TensorFlow and PyTorch.
'Embedded Systems' delves into microcontroller architecture, real-time operating systems, hardware-software co-design, and IoT applications. This course prepares students for roles in embedded software development, robotics, and smart device engineering.
'Control Systems' focuses on mathematical modeling, stability analysis, feedback control, and system design using modern tools like MATLAB/Simulink. Students learn to design controllers for industrial processes and automation systems.
'Advanced Manufacturing' introduces topics such as additive manufacturing, precision machining, lean production, and Industry 4.0 technologies. This course combines theoretical concepts with lab sessions on 3D printing and CNC machines.
'Signal Processing' covers digital signal processing techniques, filter design, spectral analysis, and applications in audio, image, and biomedical signals. Students gain proficiency in using DSP tools like MATLAB and LabVIEW.
'Computer Vision' focuses on image processing, object detection, facial recognition, and autonomous navigation systems. This course includes practical implementation of computer vision algorithms using OpenCV and deep learning frameworks.
'Data Analytics' emphasizes statistical modeling, data mining, predictive analytics, and business intelligence tools like Tableau and Power BI. Students learn to extract insights from large datasets and apply them to real-world decision-making processes.
'Cybersecurity' covers network security protocols, encryption techniques, threat analysis, penetration testing, and incident response strategies. This course prepares students for careers in cybersecurity consulting and digital forensics.
'Renewable Energy Systems' explores solar, wind, hydroelectric, and geothermal power generation technologies. Students study energy storage systems, grid integration, and policy frameworks supporting clean energy adoption.
'Biomechanics' investigates the mechanical behavior of biological systems using engineering principles. This course includes biomechanical modeling, motion analysis, and applications in prosthetics and rehabilitation devices.
The department strongly advocates for project-based learning as a cornerstone of engineering education. Students engage in both mini-projects during their second year and a capstone project in their final year. These projects are designed to simulate real-world engineering challenges, fostering creativity, teamwork, and professional communication skills.
Mini-projects are typically completed within 6-8 weeks and involve teams of 3-5 students working under faculty supervision. Projects may include designing a simple robot, developing an app prototype, or conducting a small-scale experiment in materials science.
The final-year capstone project is a comprehensive endeavor that spans the entire academic year. Students select projects based on industry needs or personal interest, often collaborating with external partners. The process involves proposal development, literature review, design phase, implementation, testing, and presentation of results.
Faculty mentors guide students through each stage of the project lifecycle, providing technical expertise, feedback, and career guidance. Regular progress meetings ensure timely completion and quality outcomes. Projects are evaluated based on innovation, feasibility, documentation, and oral presentation skills.
