Course Structure Overview
The curriculum for the Artificial Intelligence program at Chinmaya Vishwavidyapeeth is designed to provide a comprehensive foundation in both theoretical and applied aspects of AI, with an emphasis on practical implementation, ethical considerations, and innovation. The program spans four years (eight semesters) and includes core courses, departmental electives, science electives, and laboratory-based learning experiences.
| Semester | Course Code | Full Course Title | Credit Structure (L-T-P-C) | Prerequisites |
|---|---|---|---|---|
| I | MATH101 | Calculus and Analytical Geometry | 3-0-0-3 | - |
| I | PHYS101 | Physics for Engineering | 3-0-0-3 | - |
| I | CS101 | Programming Fundamentals | 2-0-2-4 | - |
| I | ENG101 | English for Technical Communication | 2-0-0-2 | - |
| I | MATH102 | Linear Algebra and Differential Equations | 3-0-0-3 | MATH101 |
| I | CS102 | Data Structures and Algorithms | 3-0-0-3 | CS101 |
| II | MATH201 | Probability and Statistics | 3-0-0-3 | MATH101 |
| II | CS201 | Object-Oriented Programming | 3-0-0-3 | CS101 |
| II | CS202 | Digital Logic Design | 3-0-0-3 | CS101 |
| II | ENG201 | Technical Writing and Presentation Skills | 2-0-0-2 | - |
| II | PHYS201 | Modern Physics | 3-0-0-3 | PHYS101 |
| II | CS203 | Database Systems | 3-0-0-3 | CS101 |
| III | CS301 | Introduction to Machine Learning | 3-0-0-3 | CS201, MATH201 |
| III | CS302 | Computational Thinking and Problem Solving | 2-0-0-2 | CS101 |
| III | CS303 | Statistical Methods in AI | 3-0-0-3 | MATH201 |
| III | CS304 | Operating Systems | 3-0-0-3 | CS201, CS202 |
| III | PHYS301 | Quantum Physics and Applications | 3-0-0-3 | PHYS201 |
| IV | CS401 | Neural Networks and Deep Learning | 3-0-0-3 | CS301, CS303 |
| IV | CS402 | Reinforcement Learning | 3-0-0-3 | CS301, MATH201 |
| IV | CS403 | Computer Vision | 3-0-0-3 | CS301, CS304 |
| IV | CS404 | Natural Language Processing | 3-0-0-3 | CS301, MATH201 |
| IV | CS405 | AI Ethics and Responsible Innovation | 2-0-0-2 | CS301 |
| V | CS501 | Advanced Topics in Machine Learning | 3-0-0-3 | CS401 |
| V | CS502 | Robotics and Automation | 3-0-0-3 | CS304, CS401 |
| V | CS503 | Computational Intelligence | 3-0-0-3 | CS401 |
| V | CS504 | AI in Healthcare Applications | 3-0-0-3 | CS401, CS403 |
| V | CS505 | Research Methodology and Project Planning | 2-0-0-2 | CS301 |
| VI | CS601 | Capstone Project I: AI Research | 4-0-0-4 | CS501, CS505 |
| VI | CS602 | Capstone Project II: Implementation and Deployment | 4-0-0-4 | CS601 |
| VI | CS603 | Internship Preparation and Career Guidance | 2-0-0-2 | - |
| VII | CS701 | Advanced Capstone Project: Industry Collaboration | 6-0-0-6 | CS602 |
| VIII | CS801 | Final Thesis/Research Dissertation | 6-0-0-6 | CS701 |
Advanced Departmental Electives
Departmental electives in the AI program allow students to deepen their understanding of specialized topics and prepare for advanced research or industry roles. These courses are taught by faculty members who are experts in their respective fields and often involve hands-on projects and collaborative research opportunities.
- Advanced Deep Learning Architectures: This course covers state-of-the-art architectures such as Transformers, GANs, and Attention Mechanisms. Students learn to implement these models using frameworks like PyTorch and TensorFlow while exploring their applications in NLP, computer vision, and speech recognition.
- Explainable AI (XAI): Focused on transparency and interpretability of AI systems, this course explores techniques for explaining model decisions. Students develop projects that integrate XAI methods into real-world scenarios to improve trust and accountability in AI deployment.
- Edge AI and Embedded Systems: Designed to explore how AI can be implemented on resource-constrained devices such as smartphones, IoT sensors, and embedded platforms. This course includes lab sessions with Raspberry Pi, Arduino, and NVIDIA Jetson Nano.
- Cognitive Modeling and Human-Machine Interaction: Combines insights from cognitive science and AI to build systems that simulate human-like interaction patterns. Students work on projects involving conversational agents, user interface design, and assistive technologies for individuals with disabilities.
- AI for Climate Change Mitigation: Addresses how AI can be leveraged to combat climate change through energy optimization, carbon footprint tracking, and environmental monitoring. Students collaborate with researchers from environmental science departments on real-world projects.
- Quantum Machine Learning: Explores the intersection of quantum computing and machine learning, including quantum algorithms for optimization and classification tasks. This course introduces students to quantum programming using Qiskit and Cirq.
- AI in Financial Markets: Covers financial applications of AI including algorithmic trading, risk modeling, fraud detection, and credit scoring. Students engage with industry professionals and use real datasets from financial institutions.
- Reinforcement Learning for Robotics: Focuses on applying RL algorithms to robot control, autonomous navigation, and manipulation tasks. Students work with robotic platforms in our lab environment to develop and test reinforcement learning policies.
- AI for Smart Cities: Examines how AI technologies can be integrated into urban infrastructure for traffic management, energy efficiency, public safety, and citizen services. Projects include smart grid simulations and predictive policing models.
- Natural Language Generation: Explores the generation of coherent and contextually appropriate text using large language models and neural text synthesis techniques. Students create tools for content creation, chatbots, and automated summarization systems.
Project-Based Learning Philosophy
The AI program at Chinmaya Vishwavidyapeeth places a strong emphasis on project-based learning as the primary mode of knowledge acquisition and skill development. The philosophy behind this approach is rooted in the belief that students learn best when they are actively engaged in solving real-world problems using AI technologies.
Mini-projects begin in the third year and continue through the final year, allowing students to explore specific areas of interest while building technical competencies. These projects are typically completed in teams and involve multiple phases including problem definition, literature review, design, implementation, testing, and documentation.
Each mini-project is supervised by a faculty mentor who provides guidance on methodology, tools, and best practices. Students are encouraged to present their work at internal symposiums, conferences, and competitions, fostering collaboration and peer feedback.
The final-year capstone project, known as the 'AI Innovation Challenge,' requires students to identify a societal challenge and propose an AI-based solution. Projects are selected based on innovation potential, technical rigor, and social impact. Successful projects may receive funding for prototyping or commercialization through our Institute’s Innovation Hub.
The evaluation criteria for projects include conceptual clarity, technical depth, documentation quality, presentation skills, peer review scores, and mentor feedback. Students must also submit a final report detailing their methodology, results, challenges encountered, and future directions.