Curriculum Overview
The engineering program at Itm Sls Baroda University Vadodara is structured over eight semesters, with a progressive curriculum designed to build both foundational knowledge and specialized expertise. Each semester includes core courses, departmental electives, science electives, and laboratory components that collectively foster an immersive learning experience.
| Semester | Course Code | Full Course Title | Credit Structure (L-T-P-C) | Prerequisites |
|---|---|---|---|---|
| 1 | ENG101 | Engineering Mathematics I | 3-1-0-4 | - |
| 1 | ENG102 | Physics for Engineers | 3-1-0-4 | - |
| 1 | ENG103 | Chemistry for Engineers | 3-1-0-4 | - |
| 1 | ENG104 | Engineering Drawing & Computer Graphics | 2-1-0-3 | - |
| 1 | ENG105 | Communication Skills | 2-0-0-2 | - |
| 1 | ENG106 | Programming for Engineers | 3-1-0-4 | - |
| 1 | ENG107 | Basic Electrical Engineering | 3-1-0-4 | - |
| 2 | ENG201 | Engineering Mathematics II | 3-1-0-4 | ENG101 |
| 2 | ENG202 | Materials Science & Metallurgy | 3-1-0-4 | - |
| 2 | ENG203 | Thermodynamics | 3-1-0-4 | - |
| 2 | ENG204 | Fluid Mechanics & Hydraulic Machines | 3-1-0-4 | - |
| 2 | ENG205 | Strength of Materials | 3-1-0-4 | - |
| 2 | ENG206 | Computer Programming & Data Structures | 3-1-0-4 | ENG106 |
| 2 | ENG207 | Electronic Devices & Circuits | 3-1-0-4 | - |
| 3 | ENG301 | Engineering Mathematics III | 3-1-0-4 | ENG201 |
| 3 | ENG302 | Design & Analysis of Algorithms | 3-1-0-4 | ENG206 |
| 3 | ENG303 | Digital Logic Design | 3-1-0-4 | - |
| 3 | ENG304 | Signals & Systems | 3-1-0-4 | - |
| 3 | ENG305 | Control Systems | 3-1-0-4 | - |
| 3 | ENG306 | Electromagnetic Fields & Waves | 3-1-0-4 | - |
| 3 | ENG307 | Manufacturing Processes | 3-1-0-4 | - |
| 4 | ENG401 | Probability & Statistics | 3-1-0-4 | ENG201 |
| 4 | ENG402 | Database Management Systems | 3-1-0-4 | ENG206 |
| 4 | ENG403 | Computer Networks | 3-1-0-4 | - |
| 4 | ENG404 | Operating Systems | 3-1-0-4 | - |
| 4 | ENG405 | Microprocessors & Microcontrollers | 3-1-0-4 | - |
| 4 | ENG406 | Machine Learning Fundamentals | 3-1-0-4 | - |
| 4 | ENG407 | Power Electronics & Drives | 3-1-0-4 | - |
| 5 | ENG501 | Advanced Mathematics for Engineers | 3-1-0-4 | ENG201 |
| 5 | ENG502 | Software Engineering & Project Management | 3-1-0-4 | - |
| 5 | ENG503 | Data Mining & Warehousing | 3-1-0-4 | - |
| 5 | ENG504 | Embedded Systems Design | 3-1-0-4 | - |
| 5 | ENG505 | Renewable Energy Systems | 3-1-0-4 | - |
| 5 | ENG506 | Robotics & Automation | 3-1-0-4 | - |
| 5 | ENG507 | Hydraulic & Pneumatic Systems | 3-1-0-4 | - |
| 6 | ENG601 | Advanced Algorithms | 3-1-0-4 | ENG302 |
| 6 | ENG602 | Computer Vision & Image Processing | 3-1-0-4 | - |
| 6 | ENG603 | Cybersecurity Principles | 3-1-0-4 | - |
| 6 | ENG604 | Artificial Intelligence | 3-1-0-4 | - |
| 6 | ENG605 | Nanotechnology & Materials Science | 3-1-0-4 | - |
| 6 | ENG606 | Industrial Design & Product Development | 3-1-0-4 | - |
| 6 | ENG607 | Power System Protection | 3-1-0-4 | - |
| 7 | ENG701 | Capstone Project I | 2-0-0-2 | - |
| 7 | ENG702 | Advanced Topics in Engineering | 3-1-0-4 | - |
| 7 | ENG703 | Research Methodology & Ethics | 3-1-0-4 | - |
| 7 | ENG704 | Entrepreneurship & Innovation | 3-1-0-4 | - |
| 7 | ENG705 | Advanced Control Systems | 3-1-0-4 | - |
| 7 | ENG706 | Biomedical Instrumentation | 3-1-0-4 | - |
| 7 | ENG707 | Environmental Engineering | 3-1-0-4 | - |
| 8 | ENG801 | Capstone Project II | 2-0-0-2 | - |
| 8 | ENG802 | Internship | 4-0-0-4 | - |
| 8 | ENG803 | Professional Practice & Ethics | 3-1-0-4 | - |
| 8 | ENG804 | Final Year Thesis | 2-0-0-2 | - |
| 8 | ENG805 | Advanced Elective I | 3-1-0-4 | - |
| 8 | ENG806 | Advanced Elective II | 3-1-0-4 | |
| 8 | ENG807 | Advanced Elective III | 3-1-0-4 |
Advanced departmental elective courses are offered in the later semesters to allow students to specialize in areas of interest. These courses include:
- Machine Learning Fundamentals: This course introduces students to supervised and unsupervised learning techniques, neural networks, deep learning frameworks, and practical applications in real-world scenarios.
- Computer Vision & Image Processing: Students learn about image segmentation, object detection, facial recognition, and computer vision algorithms used in autonomous vehicles and medical imaging systems.
- Cybersecurity Principles: The course covers encryption methods, network security protocols, ethical hacking, and incident response strategies to protect digital assets.
- Artificial Intelligence: This course delves into AI concepts including robotics, natural language processing, and reinforcement learning with hands-on projects.
- Nanotechnology & Materials Science: Students explore the behavior of matter at atomic and molecular scales, focusing on nanomaterials synthesis and applications in electronics and medicine.
- Advanced Algorithms: The course focuses on designing and analyzing complex algorithms, including graph theory, dynamic programming, and approximation algorithms.
- Robotics & Automation: Students study robot kinematics, control systems, sensor integration, and automation technologies used in manufacturing and logistics.
- Embedded Systems Design: This course teaches students how to design and program embedded systems using microcontrollers, real-time operating systems, and hardware-software integration.
- Data Mining & Warehousing: The course covers data preprocessing, clustering, classification, association rule mining, and data warehouse architecture for business intelligence.
- Software Engineering & Project Management: Students learn about software development lifecycle, agile methodologies, risk management, and project planning tools.
The philosophy of project-based learning at Itm Sls Baroda University Vadodara emphasizes collaborative problem-solving, innovation, and real-world impact. Mini-projects are introduced in the third year, allowing students to apply theoretical knowledge to practical challenges. These projects are assessed based on innovation, technical execution, documentation quality, and team collaboration.
Final-year capstone projects are more comprehensive, requiring students to undertake independent research or develop a full-scale solution to a complex engineering problem. Students are paired with faculty mentors who guide them through the research process, methodology, experimentation, and presentation of findings. The evaluation criteria include originality, technical depth, feasibility, impact, and communication skills.
Students can select their projects based on personal interest, mentor availability, or industry collaboration opportunities. The university encourages interdisciplinary projects that integrate knowledge from multiple domains to address complex societal issues.