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Fees
₹12,00,000
Placement
92.5%
Avg Package
₹6,50,000
Highest Package
₹15,00,000
Fees
₹12,00,000
Placement
92.5%
Avg Package
₹6,50,000
Highest Package
₹15,00,000
Seats
120
Students
600
Seats
120
Students
600
The Engineering program at Indira Gandhi Technological And Medical Science University Lower Subansiri is structured over eight semesters, with each semester comprising a mix of core courses, departmental electives, science electives, and laboratory sessions. The curriculum follows a progressive learning model that builds upon foundational knowledge and transitions into specialized fields.
| Semester | Course Code | Course Title | Credit Structure (L-T-P-C) | Prerequisites |
|---|---|---|---|---|
| I | ENG101 | Engineering Mathematics I | 3-1-0-4 | None |
| I | ENG102 | Basic Electrical Engineering | 3-1-0-4 | None |
| I | ENG103 | Engineering Physics | 3-1-0-4 | None |
| I | ENG104 | Chemistry for Engineers | 3-1-0-4 | None |
| I | ENG105 | Introduction to Programming | 2-0-2-3 | None |
| I | ENG106 | Engineering Drawing & Graphics | 2-0-2-3 | None |
| II | ENG201 | Engineering Mathematics II | 3-1-0-4 | ENG101 |
| II | ENG202 | Electronic Devices & Circuits | 3-1-0-4 | ENG102 |
| II | ENG203 | Mechanics of Solids | 3-1-0-4 | ENG101 |
| II | ENG204 | Thermodynamics | 3-1-0-4 | ENG101 |
| II | ENG205 | Data Structures & Algorithms | 2-0-2-3 | ENG105 |
| III | ENG301 | Signals & Systems | 3-1-0-4 | ENG201 |
| III | ENG302 | Digital Electronics | 3-1-0-4 | ENG202 |
| III | ENG303 | Materials Science | 3-1-0-4 | ENG104 |
| III | ENG304 | Fluid Mechanics | 3-1-0-4 | ENG203 |
| III | ENG305 | Object-Oriented Programming | 2-0-2-3 | ENG105 |
| IV | ENG401 | Control Systems | 3-1-0-4 | ENG301 |
| IV | ENG402 | Electromagnetic Fields | 3-1-0-4 | ENG202 |
| IV | ENG403 | Strength of Materials | 3-1-0-4 | ENG203 |
| IV | ENG404 | Heat Transfer | 3-1-0-4 | ENG204 |
| IV | ENG405 | Database Management Systems | 2-0-2-3 | ENG305 |
| V | ENG501 | Microprocessors & Interfacing | 3-1-0-4 | ENG402 |
| V | ENG502 | Design of Experiments | 3-1-0-4 | ENG401 |
| V | ENG503 | Advanced Mathematics | 3-1-0-4 | ENG201 |
| V | ENG504 | Computer Networks | 3-1-0-4 | ENG405 |
| V | ENG505 | Project Management | 2-0-2-3 | None |
| VI | ENG601 | Machine Learning | 3-1-0-4 | ENG503 |
| VI | ENG602 | Cybersecurity Fundamentals | 3-1-0-4 | ENG504 |
| VI | ENG603 | Renewable Energy Systems | 3-1-0-4 | ENG404 |
| VI | ENG604 | Embedded Systems | 3-1-0-4 | ENG501 |
| VI | ENG605 | Human Resource Management | 2-0-2-3 | None |
| VII | ENG701 | Advanced Data Analytics | 3-1-0-4 | ENG601 |
| VII | ENG702 | Neural Networks & Deep Learning | 3-1-0-4 | ENG601 |
| VII | ENG703 | Advanced Control Systems | 3-1-0-4 | ENG401 |
| VII | ENG704 | Robotics & Automation | 3-1-0-4 | ENG501 |
| VII | ENG705 | Entrepreneurship & Innovation | 2-0-2-3 | None |
| VIII | ENG801 | Final Year Project | 4-0-0-6 | ENG701, ENG702 |
| VIII | ENG802 | Capstone Seminar | 1-0-0-2 | ENG801 |
| VIII | ENG803 | Industry Internship | 0-0-0-6 | None |
| VIII | ENG804 | Professional Ethics | 1-0-0-2 | None |
| VIII | ENG805 | Advanced Topics in Engineering | 3-1-0-4 | ENG701, ENG702 |
The department offers a range of advanced departmental electives that allow students to deepen their knowledge in specialized areas. These courses are designed to align with industry trends and research directions.
This course provides an in-depth exploration of machine learning techniques, covering supervised and unsupervised learning algorithms, neural networks, deep learning frameworks, and reinforcement learning. Students will gain hands-on experience through practical assignments and real-world datasets.
Designed to equip students with essential cybersecurity knowledge, this course covers network security protocols, cryptographic techniques, penetration testing methodologies, and incident response strategies. Practical labs simulate real-world threat scenarios for skill development.
This elective explores the design and implementation of renewable energy technologies including solar, wind, hydroelectric, and geothermal systems. Students learn about energy storage solutions, grid integration challenges, and environmental impact assessments.
Students are introduced to embedded system architecture, real-time operating systems, microcontroller programming, and hardware-software co-design. The course includes laboratory sessions where students build functional embedded applications using ARM processors and IoT platforms.
This course focuses on advanced analytics techniques such as predictive modeling, statistical inference, data visualization, and big data processing using tools like Hadoop and Spark. Students apply these methods to solve complex business problems.
A comprehensive study of neural networks, including feedforward, convolutional, recurrent, and transformer architectures. Students implement models for image recognition, natural language processing, and time series forecasting using TensorFlow and PyTorch.
This course delves into modern control theory, including optimal control, robust control, and adaptive control strategies. It includes practical applications in robotics, automation, and industrial process control.
Students explore the fundamentals of robotics, including kinematics, dynamics, sensor integration, and autonomous navigation. The course combines theoretical concepts with hands-on projects involving robotic arms, drones, and mobile robots.
The department believes in integrating project-based learning into every stage of the curriculum to ensure students develop practical skills alongside theoretical knowledge. Projects are assigned based on real-world engineering challenges that reflect industry needs and societal concerns.
Mini-projects are introduced from the second semester, allowing students to experiment with concepts learned in class. These projects are typically completed in teams and are evaluated based on innovation, technical execution, documentation quality, and presentation skills.
The final-year thesis/capstone project is a significant component of the program, requiring students to conduct original research or develop a comprehensive engineering solution. Students work closely with faculty mentors who guide them through literature review, methodology design, experimentation, and result analysis.
Project selection involves discussions between students and faculty members, considering student interests, available resources, and alignment with current research trends. The evaluation criteria include progress reports, peer reviews, and final presentations to an external panel of experts.
