Comprehensive Course Structure
| Semester | Course Code | Course Title | Credits (L-T-P-C) | Pre-requisites |
|---|---|---|---|---|
| 1 | MATH101 | Calculus I | 3-1-0-4 | - |
| 1 | PHYS101 | Physics I | 3-1-0-4 | - |
| 1 | CHEM101 | Chemistry I | 3-1-0-4 | - |
| 1 | ENG101 | English Communication Skills | 2-0-0-2 | - |
| 1 | CSE101 | Introduction to Programming | 3-0-2-4 | - |
| 1 | MECH101 | Engineering Drawing | 2-0-2-3 | - |
| 1 | LAB101 | Programming Lab | 0-0-4-2 | - |
| 2 | MATH201 | Calculus II | 3-1-0-4 | MATH101 |
| 2 | PHYS201 | Physics II | 3-1-0-4 | PHYS101 |
| 2 | CHEM201 | Chemistry II | 3-1-0-4 | CHEM101 |
| 2 | CSE201 | Data Structures & Algorithms | 3-1-0-4 | CSE101 |
| 2 | MECH201 | Mechanics of Materials | 3-1-0-4 | - |
| 2 | LAB201 | Data Structures Lab | 0-0-4-2 | CSE101 |
| 3 | MATH301 | Probability & Statistics | 3-1-0-4 | MATH201 |
| 3 | PHYS301 | Electromagnetic Theory | 3-1-0-4 | PHYS201 |
| 3 | CSE301 | Digital Logic & Design | 3-1-0-4 | CSE201 |
| 3 | MECH301 | Thermodynamics | 3-1-0-4 | MECH201 |
| 3 | CSE302 | Database Management Systems | 3-1-0-4 | CSE201 |
| 3 | LAB301 | Digital Logic Lab | 0-0-4-2 | - |
| 4 | MATH401 | Linear Algebra | 3-1-0-4 | MATH301 |
| 4 | CSE401 | Operating Systems | 3-1-0-4 | CSE301 |
| 4 | MECH401 | Fluid Mechanics | 3-1-0-4 | MECH301 |
| 4 | EE401 | Electrical Circuits & Networks | 3-1-0-4 | - |
| 4 | CSE402 | Computer Architecture | 3-1-0-4 | CSE301 |
| 4 | LAB401 | Operating Systems Lab | 0-0-4-2 | CSE401 |
| 5 | CSE501 | Software Engineering | 3-1-0-4 | CSE401 |
| 5 | MECH501 | Mechanical Design | 3-1-0-4 | MECH401 |
| 5 | CSE502 | Machine Learning Fundamentals | 3-1-0-4 | CSE301 |
| 5 | EE501 | Signals & Systems | 3-1-0-4 | PHYS301 |
| 5 | CSE503 | Web Technologies | 3-1-0-4 | CSE401 |
| 5 | LAB501 | Software Engineering Lab | 0-0-4-2 | CSE501 |
| 6 | CSE601 | Deep Learning | 3-1-0-4 | CSE502 |
| 6 | MECH601 | Advanced Manufacturing Processes | 3-1-0-4 | MECH501 |
| 6 | CSE602 | Cybersecurity | 3-1-0-4 | CSE501 |
| 6 | EE601 | Control Systems | 3-1-0-4 | EE501 |
| 6 | CSE603 | Big Data Analytics | 3-1-0-4 | CSE501 |
| 6 | LAB601 | Deep Learning Lab | 0-0-4-2 | CSE601 |
| 7 | CSE701 | Capstone Project I | 3-0-0-3 | CSE501 |
| 7 | MECH701 | Special Topics in Mechanical Engineering | 3-1-0-4 | MECH601 |
| 7 | CSE702 | Research Methodology | 2-0-0-2 | - |
| 7 | EE701 | Power Electronics | 3-1-0-4 | EE601 |
| 7 | CSE703 | Advanced Web Applications | 3-1-0-4 | CSE503 |
| 7 | LAB701 | Research Lab | 0-0-4-2 | - |
| 8 | CSE801 | Capstone Project II | 3-0-0-3 | CSE701 |
| 8 | MECH801 | Industrial Training | 0-0-0-4 | - |
| 8 | CSE802 | Entrepreneurship & Innovation | 2-0-0-2 | - |
| 8 | EE801 | Advanced Control Systems | 3-1-0-4 | EE701 |
| 8 | CSE803 | Capstone Thesis Writing | 2-0-0-2 | CSE702 |
| 8 | LAB801 | Final Project Lab | 0-0-4-2 | - |
Advanced Departmental Elective Courses
These courses are designed to deepen student understanding in specialized areas of engineering and technology. They provide opportunities for advanced study, independent research, and real-world application.
- Deep Learning: This course explores the mathematical foundations of deep learning models including neural networks, convolutional networks, recurrent networks, transformers, and generative adversarial networks (GANs). Students will implement models using TensorFlow and PyTorch, analyze performance metrics, and explore applications in computer vision and natural language processing.
- Advanced Robotics: This course covers advanced topics in robot kinematics, dynamics, control systems, and sensor integration. Students will design and build robotic systems capable of autonomous navigation, manipulation tasks, and human-robot interaction using ROS (Robot Operating System).
- Cybersecurity & Network Security: This course examines modern threats in networked environments and defensive strategies including encryption, authentication protocols, intrusion detection systems, and secure software development practices. Students will conduct penetration testing, analyze vulnerabilities, and implement security policies.
- Power Electronics & Drives: This course focuses on the design and analysis of power conversion circuits used in renewable energy systems, electric vehicles, and industrial drives. Topics include DC-DC converters, AC-DC rectifiers, inverters, and motor control techniques.
- Data Mining and Knowledge Discovery: This course introduces students to techniques for extracting patterns from large datasets using clustering, classification, association rule mining, and anomaly detection algorithms. Students will apply these methods to real-world problems in business intelligence, healthcare, and scientific research.
- Control Systems Design: This course covers the principles of feedback control systems, stability analysis, frequency response techniques, and modern control theory including state-space representation and optimal control. Students will design controllers for dynamic systems using MATLAB/Simulink and implement them on hardware platforms.
- Sustainable Energy Technologies: This course explores sustainable energy solutions including solar thermal and photovoltaic systems, wind turbines, hydroelectric generation, and geothermal energy conversion. Students will model energy systems, evaluate performance, and design hybrid configurations for different applications.
- Biomedical Signal Processing: This course focuses on processing physiological signals such as ECG, EEG, EMG, and fMRI using digital signal processing techniques. Students will learn to extract features from biomedical data, develop diagnostic algorithms, and interface with medical devices.
- Advanced Materials for Engineering Applications: This course covers the structure, properties, and applications of advanced materials including composites, ceramics, polymers, and nanomaterials. Students will study synthesis techniques, characterization methods, and design principles for engineering applications in aerospace, automotive, and biomedical fields.
- Internet of Things (IoT) & Embedded Systems: This course explores IoT architecture, sensor networks, wireless communication protocols, embedded programming, and edge computing platforms. Students will build IoT applications using microcontrollers, Raspberry Pi, Arduino, and cloud services like AWS IoT Core and Azure IoT Hub.
Project-Based Learning Philosophy
The department believes that project-based learning is essential for developing critical thinking, problem-solving skills, and technical proficiency among students. Projects are designed to be collaborative, interdisciplinary, and aligned with real-world challenges in industry and society.
Mini-Projects
Mini-projects begin in the second year and continue through the third year. These projects are typically completed in teams of 3-5 students under the supervision of a faculty mentor. The goal is to apply theoretical knowledge to practical problems, develop communication skills, and gain experience in project management.
Final-Year Thesis/Capstone Project
The final-year capstone project is a significant component of the program. Students select a topic related to their specialization and work closely with a faculty advisor for 12 months. The project culminates in a written thesis, an oral presentation, and a demonstration of the developed solution.
Project Selection Process
Students choose projects based on their interests, academic background, and career goals. They are encouraged to propose ideas that address real-world problems or align with ongoing research initiatives at the institute. Faculty members guide students in refining project scope, setting objectives, and planning implementation strategies.