Comprehensive Course Structure Across 8 Semesters
| Semester | Course Code | Course Title | Credits (L-T-P-C) | Prerequisites |
|---|---|---|---|---|
| 1 | ENG101 | Engineering Graphics | 2-0-0-2 | - |
| 1 | MAT101 | Mathematics I | 3-0-0-3 | - |
| 1 | PHY101 | Physics | 3-0-0-3 | - |
| 1 | CHE101 | Chemistry | 3-0-0-3 | - |
| 1 | BIO101 | Biology for Engineers | 2-0-0-2 | - |
| 1 | ENG102 | Basic Electrical Engineering | 3-0-0-3 | - |
| 1 | CS101 | Programming Fundamentals | 3-0-0-3 | - |
| 2 | MAT201 | Mathematics II | 3-0-0-3 | MAT101 |
| 2 | PHY201 | Physics II | 3-0-0-3 | PHY101 |
| 2 | CHE201 | Chemistry II | 3-0-0-3 | CHE101 |
| 2 | ENG201 | Electronics Devices and Circuits | 3-0-0-3 | ENG102 |
| 2 | CS201 | Data Structures & Algorithms | 3-0-0-3 | CS101 |
| 2 | ENG202 | Mechanics of Materials | 3-0-0-3 | - |
| 3 | MAT301 | Mathematics III | 3-0-0-3 | MAT201 |
| 3 | ENG301 | Signals and Systems | 3-0-0-3 | - |
| 3 | ENG302 | Thermodynamics | 3-0-0-3 | - |
| 3 | CS301 | Database Management Systems | 3-0-0-3 | CS201 |
| 3 | ENG303 | Fluid Mechanics | 3-0-0-3 | - |
| 4 | MAT401 | Mathematics IV | 3-0-0-3 | MAT301 |
| 4 | ENG401 | Electromagnetic Fields | 3-0-0-3 | - |
| 4 | CS401 | Software Engineering | 3-0-0-3 | CS301 |
| 4 | ENG402 | Machine Design | 3-0-0-3 | - |
| 5 | ENG501 | Control Systems | 3-0-0-3 | - |
| 5 | CS501 | Computer Networks | 3-0-0-3 | CS401 |
| 5 | ENG502 | Power Systems | 3-0-0-3 | - |
| 5 | ENG503 | Structural Analysis | 3-0-0-3 | - |
| 6 | ENG601 | Advanced Control Systems | 3-0-0-3 | ENG501 |
| 6 | CS601 | Machine Learning | 3-0-0-3 | CS501 |
| 6 | ENG602 | Renewable Energy Systems | 3-0-0-3 | - |
| 6 | ENG603 | Advanced Structural Design | 3-0-0-3 | ENG503 |
| 7 | ENG701 | Research Methodology | 2-0-0-2 | - |
| 7 | CS701 | Deep Learning | 3-0-0-3 | CS601 |
| 7 | ENG702 | Project Management | 2-0-0-2 | - |
| 8 | ENG801 | Final Year Project | 6-0-0-6 | - |
| 8 | CS801 | Capstone Project | 4-0-0-4 | - |
Detailed Overview of Advanced Departmental Electives
Advanced departmental electives are offered in each specialization track to provide in-depth knowledge and skills relevant to industry demands. These courses allow students to explore niche areas within their field, enhancing both academic and professional growth.
- Deep Learning with TensorFlow: This course introduces students to neural networks, convolutional networks, recurrent networks, and reinforcement learning using TensorFlow. It includes practical implementation of image recognition and natural language processing tasks.
- Computer Vision Applications: Focused on visual data analysis, this elective covers object detection, segmentation, tracking, and 3D reconstruction techniques used in robotics, autonomous vehicles, and medical imaging.
- Cybersecurity for IoT Devices: As the Internet of Things expands, so does the need for securing connected devices. This course explores vulnerabilities in embedded systems and develops secure protocols to protect smart infrastructure.
- Blockchain Technology & Applications: Students learn about distributed ledger technology, smart contracts, consensus mechanisms, and how blockchain can be applied in supply chain management, healthcare, and finance sectors.
- Renewable Energy Systems Integration: This elective delves into solar, wind, and hydroelectric systems, focusing on grid integration challenges, energy storage solutions, and policy frameworks supporting clean energy transition.
- Advanced Materials in Engineering: Covers nanomaterials, composite structures, smart materials, and their applications in aerospace, biomedical, and automotive industries. Includes lab sessions using advanced characterization tools.
- Smart Grid Technologies: Explores modern grid architecture, demand response systems, microgrids, and energy management platforms that support sustainable electricity distribution.
- Robotics and Automation Systems: Students design and build robotic systems for industrial automation, including sensor integration, control algorithms, and machine learning-based motion planning.
- Biomedical Instrumentation: Teaches students how to develop medical devices such as ECG monitors, blood glucose meters, and MRI machines using engineering principles and biocompatibility standards.
- Environmental Impact Assessment: Focuses on evaluating the ecological consequences of engineering projects through modeling software, policy analysis, and mitigation strategies for sustainable development.
Project-Based Learning Philosophy
At Amity University Patna, project-based learning is central to our educational philosophy. We believe that real-world experiences are essential for developing critical thinking, creativity, and teamwork skills.
The structure of our projects follows a multi-stage approach: idea generation, research and design, prototyping, testing, documentation, and presentation. Students work in teams and receive guidance from faculty mentors throughout the process.
Mini-projects are assigned in the second year to introduce students to basic engineering challenges. These projects are typically completed within 4-6 weeks and involve small-scale experiments or simulations.
The final-year thesis/capstone project is a significant component of the program, spanning 12-16 weeks. It requires students to tackle a complex problem from industry or academia, often resulting in publishable work or patentable innovations.
Students select projects based on their interests and career goals, with faculty mentors assigned according to expertise alignment. Evaluation criteria include innovation, feasibility, presentation quality, and adherence to deadlines.