Curriculum Overview
The engineering curriculum at Mohan Babu University Tirupati is meticulously designed to provide students with a robust foundation in core engineering principles while enabling them to explore specialized areas of interest. The program spans four academic years, divided into eight semesters, each containing a carefully curated set of courses that build upon one another.
| Semester | Course Code | Course Title | Credit Structure (L-T-P-C) | Prerequisites |
|---|---|---|---|---|
| 1 | EN101 | Engineering Mathematics I | 3-1-0-4 | None |
| 1 | EN102 | Physics for Engineers | 3-1-0-4 | None |
| 1 | EN103 | Chemistry for Engineers | 3-1-0-4 | None |
| 1 | EN104 | Engineering Graphics & Design | 2-1-0-3 | None |
| 1 | EN105 | Programming Fundamentals | 2-0-2-4 | None |
| 1 | EN106 | Basic Electrical Circuits | 3-1-0-4 | None |
| 2 | EN201 | Engineering Mathematics II | 3-1-0-4 | EN101 |
| 2 | EN202 | Mechanics of Materials | 3-1-0-4 | EN106 |
| 2 | EN203 | Thermodynamics | 3-1-0-4 | EN102 |
| 2 | EN204 | Fluid Mechanics | 3-1-0-4 | EN102 |
| 2 | EN205 | Data Structures & Algorithms | 2-0-2-4 | EN105 |
| 2 | EN206 | Circuit Analysis | 3-1-0-4 | EN106 |
| 3 | EN301 | Machine Design | 3-1-0-4 | EN202 |
| 3 | EN302 | Control Systems | 3-1-0-4 | EN206 |
| 3 | EN303 | Signals & Systems | 3-1-0-4 | EN201 |
| 3 | EN304 | Structural Analysis | 3-1-0-4 | EN202 |
| 3 | EN305 | Computer Architecture | 3-1-0-4 | EN205 |
| 3 | EN306 | Electromagnetic Fields | 3-1-0-4 | EN206 |
| 4 | EN401 | Advanced Machine Design | 3-1-0-4 | EN301 |
| 4 | EN402 | Power Electronics | 3-1-0-4 | EN306 |
| 4 | EN403 | Embedded Systems | 3-1-0-4 | EN305 |
| 4 | EN404 | Transportation Engineering | 3-1-0-4 | EN204 |
| 4 | EN405 | Digital Signal Processing | 3-1-0-4 | EN303 |
| 4 | EN406 | Renewable Energy Technologies | 3-1-0-4 | EN203 |
| 5 | EN501 | Deep Learning | 3-1-0-4 | EN305 |
| 5 | EN502 | Natural Language Processing | 3-1-0-4 | EN501 |
| 5 | EN503 | Reinforcement Learning | 3-1-0-4 | EN501 |
| 5 | EN504 | Cybersecurity Fundamentals | 3-1-0-4 | EN205 |
| 5 | EN505 | Software Testing & Quality Assurance | 3-1-0-4 | EN305 |
| 5 | EN506 | Data Visualization | 3-1-0-4 | EN205 |
| 6 | EN601 | Robotics and Automation | 3-1-0-4 | EN302 |
| 6 | EN602 | Advanced Manufacturing Techniques | 3-1-0-4 | EN301 |
| 6 | EN603 | Smart Materials and Structures | 3-1-0-4 | EN202 |
| 6 | EN604 | Finite Element Analysis | 3-1-0-4 | EN202 |
| 6 | EN605 | Environmental Impact Assessment | 3-1-0-4 | EN204 |
| 6 | EN606 | Sustainable Urban Planning | 3-1-0-4 | EN204 |
| 7 | EN701 | Advanced Control Systems | 3-1-0-4 | EN302 |
| 7 | EN702 | Power System Protection | 3-1-0-4 | EN306 |
| 7 | EN703 | Renewable Energy Systems | 3-1-0-4 | EN203 |
| 7 | EN704 | Advanced Communication Systems | 3-1-0-4 | EN303 |
| 7 | EN705 | Signal Processing Applications | 3-1-0-4 | EN303 |
| 7 | EN706 | Sustainable Infrastructure | 3-1-0-4 | EN204 |
| 8 | EN801 | Final Year Project | 0-0-6-6 | All previous courses |
| 8 | EN802 | Research Methodology | 3-1-0-4 | EN501 |
| 8 | EN803 | Capstone Thesis | 0-0-6-6 | All previous courses |
| 8 | EN804 | Industry Internship | 0-0-0-3 | All previous courses |
Detailed Elective Course Descriptions
The department offers several advanced elective courses that allow students to specialize in emerging fields and explore innovative technologies. These courses are taught by experienced faculty members who are leaders in their domains.
Deep Learning: This course covers fundamental concepts of neural networks, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers. Students will gain hands-on experience with frameworks like TensorFlow and PyTorch and work on real-world projects such as image recognition and natural language processing.
Natural Language Processing: Focused on the intersection of linguistics and artificial intelligence, this course explores techniques for understanding and generating human language using machine learning models. Topics include sentiment analysis, named entity recognition, and text summarization.
Reinforcement Learning: This elective introduces students to reinforcement learning algorithms used in robotics, game theory, and autonomous systems. The course includes theoretical foundations and practical implementation using tools like OpenAI Gym and Stable Baselines3.
Cybersecurity Fundamentals: Designed for students interested in protecting digital assets, this course covers topics such as network security, cryptography, malware analysis, and ethical hacking. Practical labs involve penetration testing and vulnerability assessment.
Software Testing & Quality Assurance: This course teaches various software testing methodologies, including unit testing, integration testing, and performance testing. Students learn to use automation tools like Selenium and JUnit to ensure high-quality software delivery.
Data Visualization: Through this course, students will master the art of presenting complex data in visually compelling ways using libraries like D3.js, Tableau, and Plotly. The curriculum emphasizes storytelling through data and creating interactive dashboards for business intelligence.
Robotics and Automation: This course explores the design and implementation of robotic systems with applications in manufacturing, healthcare, and exploration. Students will build robots using microcontrollers, sensors, and actuators while programming them to perform complex tasks.
Advanced Manufacturing Techniques: Focused on modern manufacturing methods such as 3D printing, laser cutting, and CNC machining, this course bridges the gap between traditional and digital fabrication. Students will learn to use CAD software and optimize manufacturing processes for cost and efficiency.
Smart Materials and Structures: This advanced course delves into materials that respond to environmental stimuli, such as shape-memory alloys and piezoelectric ceramics. Students will study their applications in aerospace, biomedical devices, and smart infrastructure.
Finite Element Analysis: This course teaches students how to model and analyze structures using finite element methods. Practical sessions involve solving engineering problems in civil and mechanical domains using software like ANSYS and ABAQUS.
Environmental Impact Assessment: Focused on evaluating the environmental consequences of development projects, this course covers methodologies for conducting environmental impact assessments (EIAs) and sustainable practices in engineering design.
Project-Based Learning Framework
Project-based learning is a cornerstone of our engineering education philosophy. From the first year, students engage in structured projects that reinforce classroom learning and develop practical skills.
Mini-projects are assigned at the end of each semester to help students apply theoretical concepts in real-world scenarios. These projects typically involve small teams and are evaluated based on design, execution, documentation, and presentation.
The final-year capstone project is a significant undertaking that allows students to integrate knowledge from multiple disciplines. Students select a topic relevant to their specialization and work closely with faculty mentors to develop innovative solutions or research findings.
Project selection involves a process where students propose ideas, receive feedback from advisors, and refine their concepts before final approval. Faculty mentors are selected based on expertise in the relevant domain, ensuring that students receive guidance aligned with current industry trends and research advancements.