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Fees
₹32,00,000
Placement
96.0%
Avg Package
₹8,00,000
Highest Package
₹16,00,000
Fees
₹32,00,000
Placement
96.0%
Avg Package
₹8,00,000
Highest Package
₹16,00,000
Seats
1,200
Students
1,200
Seats
1,200
Students
1,200
The curriculum at Rai Technology University Bangalore is meticulously designed to provide students with a robust foundation in engineering principles, while also offering flexibility to explore specialized areas of interest. The program is structured over eight semesters, with each semester building upon the previous one to ensure a progressive learning experience. The curriculum integrates core engineering disciplines with emerging technologies and industry practices, ensuring that students are well-prepared for the demands of the modern engineering landscape.
Each semester includes a mix of core courses, departmental electives, science electives, and laboratory sessions. Core courses are fundamental to the engineering discipline and provide students with essential knowledge in mathematics, physics, chemistry, and engineering principles. Departmental electives allow students to specialize in their chosen field, while science electives broaden their understanding of related scientific disciplines. Laboratory sessions are integral to the curriculum, providing hands-on experience and reinforcing theoretical concepts through practical application.
The following table outlines the detailed course structure for each semester:
| Semester | Course Code | Course Title | Credit Structure (L-T-P-C) | Prerequisites |
|---|---|---|---|---|
| 1 | ENG101 | Engineering Mathematics I | 3-1-0-4 | None |
| 1 | ENG102 | Engineering Physics | 3-1-0-4 | None |
| 1 | ENG103 | Engineering Chemistry | 3-1-0-4 | None |
| 1 | ENG104 | Engineering Graphics | 2-1-0-3 | None |
| 1 | ENG105 | Programming and Problem Solving | 3-0-2-4 | None |
| 1 | ENG106 | Engineering Mechanics | 3-1-0-4 | None |
| 1 | ENG107 | Environmental Studies | 2-0-0-2 | None |
| 1 | ENG108 | Technical English | 2-0-0-2 | None |
| 2 | ENG201 | Engineering Mathematics II | 3-1-0-4 | ENG101 |
| 2 | ENG202 | Electrical Circuits and Networks | 3-1-0-4 | ENG102 |
| 2 | ENG203 | Material Science | 3-1-0-4 | ENG103 |
| 2 | ENG204 | Thermodynamics | 3-1-0-4 | ENG106 |
| 2 | ENG205 | Fluid Mechanics | 3-1-0-4 | ENG106 |
| 2 | ENG206 | Data Structures and Algorithms | 3-0-2-4 | ENG105 |
| 2 | ENG207 | Engineering Economics | 2-0-0-2 | None |
| 2 | ENG208 | Introduction to Programming | 2-0-2-3 | ENG105 |
| 3 | ENG301 | Engineering Mathematics III | 3-1-0-4 | ENG201 |
| 3 | ENG302 | Control Systems | 3-1-0-4 | ENG202 |
| 3 | ENG303 | Signals and Systems | 3-1-0-4 | ENG201 |
| 3 | ENG304 | Manufacturing Processes | 3-1-0-4 | ENG106 |
| 3 | ENG305 | Electromagnetic Fields | 3-1-0-4 | ENG202 |
| 3 | ENG306 | Software Engineering | 3-0-2-4 | ENG206 |
| 3 | ENG307 | Engineering Management | 2-0-0-2 | ENG207 |
| 3 | ENG308 | Microprocessors and Microcontrollers | 3-0-2-4 | ENG202 |
| 4 | ENG401 | Engineering Mathematics IV | 3-1-0-4 | ENG301 |
| 4 | ENG402 | Power Systems | 3-1-0-4 | ENG202 |
| 4 | ENG403 | Heat Transfer | 3-1-0-4 | ENG204 |
| 4 | ENG404 | Structural Analysis | 3-1-0-4 | ENG106 |
| 4 | ENG405 | Computer Architecture | 3-1-0-4 | ENG308 |
| 4 | ENG406 | Database Management Systems | 3-0-2-4 | ENG206 |
| 4 | ENG407 | Operations Research | 3-1-0-4 | ENG301 |
| 4 | ENG408 | Advanced Programming | 3-0-2-4 | ENG206 |
| 5 | ENG501 | Advanced Mathematics | 3-1-0-4 | ENG401 |
| 5 | ENG502 | Renewable Energy Systems | 3-1-0-4 | ENG402 |
| 5 | ENG503 | Advanced Control Systems | 3-1-0-4 | ENG302 |
| 5 | ENG504 | Advanced Materials | 3-1-0-4 | ENG303 |
| 5 | ENG505 | Advanced Signal Processing | 3-1-0-4 | ENG303 |
| 5 | ENG506 | Machine Learning | 3-0-2-4 | ENG406 |
| 5 | ENG507 | Project Management | 2-0-0-2 | ENG307 |
| 5 | ENG508 | Embedded Systems | 3-0-2-4 | ENG308 |
| 6 | ENG601 | Advanced Thermodynamics | 3-1-0-4 | ENG403 |
| 6 | ENG602 | Advanced Fluid Mechanics | 3-1-0-4 | ENG405 |
| 6 | ENG603 | Advanced Structural Design | 3-1-0-4 | ENG404 |
| 6 | ENG604 | Advanced Computer Networks | 3-1-0-4 | ENG405 |
| 6 | ENG605 | Advanced Database Systems | 3-0-2-4 | ENG406 |
| 6 | ENG606 | Deep Learning | 3-0-2-4 | ENG506 |
| 6 | ENG607 | Supply Chain Management | 2-0-0-2 | ENG307 |
| 6 | ENG608 | Robotics | 3-0-2-4 | ENG508 |
| 7 | ENG701 | Research Methodology | 2-0-0-2 | None |
| 7 | ENG702 | Advanced Renewable Energy | 3-1-0-4 | ENG502 |
| 7 | ENG703 | Advanced Power Electronics | 3-1-0-4 | ENG402 |
| 7 | ENG704 | Advanced Heat Transfer | 3-1-0-4 | ENG601 |
| 7 | ENG705 | Advanced Materials Science | 3-1-0-4 | ENG504 |
| 7 | ENG706 | Advanced Machine Learning | 3-0-2-4 | ENG506 |
| 7 | ENG707 | Advanced Project Management | 2-0-0-2 | ENG507 |
| 7 | ENG708 | Advanced Embedded Systems | 3-0-2-4 | ENG508 |
| 8 | ENG801 | Capstone Project | 4-0-0-4 | ENG701 |
| 8 | ENG802 | Industry Internship | 4-0-0-4 | None |
| 8 | ENG803 | Advanced Research | 4-0-0-4 | ENG701 |
| 8 | ENG804 | Professional Ethics | 2-0-0-2 | None |
Departmental elective courses are designed to provide students with in-depth knowledge in specialized areas of engineering. These courses are offered in the fifth and sixth semesters, allowing students to explore their interests and align their studies with career goals. The following are detailed descriptions of several advanced departmental elective courses:
This course provides students with a comprehensive understanding of machine learning algorithms and their applications. The learning objectives include mastering supervised and unsupervised learning techniques, understanding neural networks and deep learning, and applying these concepts to real-world problems. The course is relevant to students interested in artificial intelligence, data science, and software engineering.
Deep learning is a subset of machine learning that focuses on neural networks with multiple layers. This course covers advanced topics such as convolutional neural networks, recurrent neural networks, and transformers. Students will learn to implement deep learning models using frameworks like TensorFlow and PyTorch. The course is highly relevant for students pursuing careers in AI, computer vision, and natural language processing.
This course introduces students to the design and implementation of embedded systems, which are specialized computing systems embedded in larger devices. Topics include microcontroller architecture, real-time operating systems, and hardware-software co-design. Students will gain hands-on experience with development tools and platforms such as Arduino and Raspberry Pi. The course is relevant for students interested in IoT, robotics, and embedded software development.
Robotics is an interdisciplinary field that combines mechanical engineering, electrical engineering, and computer science. This course covers robot kinematics, control systems, sensor integration, and autonomous navigation. Students will work on projects involving robot design and programming, using tools such as ROS (Robot Operating System). The course is relevant for students interested in automation, artificial intelligence, and robotics engineering.
This course explores advanced topics in database design and management, including distributed databases, data warehousing, and data mining. Students will learn to design and implement complex database systems using SQL and NoSQL technologies. The course is relevant for students interested in data engineering, database administration, and big data analytics.
Computer networks form the backbone of modern communication systems. This course covers advanced topics such as network security, quality of service, and wireless networks. Students will learn to design and analyze complex network architectures and protocols. The course is relevant for students interested in network engineering, cybersecurity, and telecommunications.
Signal processing is essential in various engineering disciplines, including audio engineering, image processing, and telecommunications. This course covers advanced techniques such as digital filtering, spectral analysis, and wavelet transforms. Students will gain hands-on experience with signal processing tools and software. The course is relevant for students interested in audio and video processing, telecommunications, and biomedical engineering.
This course delves into the structure, properties, and applications of advanced materials. Topics include nanomaterials, composite materials, and smart materials. Students will learn to analyze material properties using computational tools and experimental techniques. The course is relevant for students interested in materials engineering, nanotechnology, and advanced manufacturing.
Thermodynamics is a fundamental area of engineering that deals with energy conversion and heat transfer. This course covers advanced topics such as thermodynamic cycles, phase equilibrium, and entropy. Students will learn to analyze complex thermodynamic systems and apply thermodynamic principles to real-world problems. The course is relevant for students interested in energy systems, power generation, and thermal engineering.
Control systems are essential in engineering applications where precise regulation is required. This course covers advanced control techniques such as state-space representation, optimal control, and robust control. Students will learn to design and analyze control systems using MATLAB and Simulink. The course is relevant for students interested in automation, robotics, and process control.
The department's philosophy on project-based learning is centered on the belief that students learn best when they are actively engaged in solving real-world problems. This approach is implemented through a structured framework that includes mini-projects in the earlier semesters and a final-year capstone project that integrates all the knowledge and skills acquired during the program.
Mini-projects are introduced in the second year, where students work in teams to design and implement solutions to specific engineering challenges. These projects are evaluated based on technical merit, creativity, teamwork, and presentation skills. The projects are often aligned with industry needs and are supervised by faculty members who provide guidance and mentorship.
The final-year thesis or capstone project is a comprehensive endeavor that requires students to conduct independent research or develop a significant engineering solution. Students select their projects based on their interests and career goals, and they are paired with faculty mentors who provide expert guidance throughout the process. The project is evaluated through a combination of written reports, oral presentations, and peer reviews.
The evaluation criteria for these projects are designed to assess not only technical competence but also critical thinking, problem-solving abilities, and communication skills. Students are encouraged to collaborate with industry partners, research institutions, and fellow students to enhance their learning experience and produce innovative solutions.
The department also provides resources and support for students to participate in national and international competitions, hackathons, and innovation challenges. These opportunities allow students to showcase their skills, gain recognition, and build professional networks.
