Curriculum Overview
The curriculum for the AI program at Birla Institute of Management Technology is meticulously structured to provide a balanced blend of theoretical knowledge and practical application. It spans eight semesters, with each semester building upon previous learnings while introducing new challenges and concepts.
| SEMESTER | COURSE CODE | COURSE TITLE | CREDIT STRUCTURE (L-T-P-C) | PREREQUISITES |
|---|---|---|---|---|
| I | MTH101 | Calculus and Linear Algebra | 3-1-0-4 | - |
| I | CSE101 | Introduction to Programming | 2-0-2-3 | - |
| I | CSE102 | Data Structures and Algorithms | 3-1-0-4 | MTH101, CSE101 |
| I | PHY101 | Physics for Engineers | 3-1-0-4 | - |
| I | CHM101 | Chemistry for Engineers | 2-1-0-3 | - |
| I | HSS101 | English Communication Skills | 2-0-0-2 | - |
| II | MTH201 | Probability and Statistics | 3-1-0-4 | MTH101 |
| II | CSE201 | Database Management Systems | 3-1-0-4 | CSE102 |
| II | CSE202 | Software Engineering | 3-1-0-4 | CSE102 |
| II | CSE203 | Digital Logic and Computer Organization | 3-1-0-4 | CSE102 |
| II | PHY201 | Optics, Waves and Modern Physics | 3-1-0-4 | PHY101 |
| III | MTH301 | Numerical Methods | 3-1-0-4 | MTH201 |
| III | CSE301 | Machine Learning Fundamentals | 3-1-0-4 | MTH201, CSE201 |
| III | CSE302 | Computer Architecture | 3-1-0-4 | CSE203 |
| III | CSE303 | Operating Systems | 3-1-0-4 | CSE201, CSE203 |
| III | CSE304 | Computer Networks | 3-1-0-4 | CSE201, CSE302 |
| IV | MTH401 | Advanced Calculus and Differential Equations | 3-1-0-4 | MTH101 |
| IV | CSE401 | Deep Learning | 3-1-0-4 | CSE301 |
| IV | CSE402 | Natural Language Processing | 3-1-0-4 | CSE301 |
| IV | CSE403 | Computer Vision | 3-1-0-4 | CSE301 |
| IV | CSE404 | Reinforcement Learning | 3-1-0-4 | CSE301 |
| V | CSE501 | AI Ethics and Responsible Innovation | 3-1-0-4 | CSE301 |
| V | CSE502 | Human-Computer Interaction | 3-1-0-4 | CSE301 |
| V | CSE503 | AI for Healthcare | 3-1-0-4 | CSE401 |
| V | CSE504 | Autonomous Systems | 3-1-0-4 | CSE401, CSE403 |
| VI | CSE601 | Quantum Machine Learning | 3-1-0-4 | CSE401 |
| VI | CSE602 | Advanced Topics in AI | 3-1-0-4 | CSE501 |
| VI | CSE603 | Research Methodology | 2-0-2-3 | - |
| VII | CSE701 | Capstone Project I | 4-0-0-4 | CSE602 |
| VIII | CSE801 | Capstone Project II | 4-0-0-4 | CSE701 |
The curriculum integrates both core and elective subjects designed to give students a comprehensive understanding of AI principles and applications. Core courses provide foundational knowledge in mathematics, computer science, and engineering disciplines essential for advanced AI studies.
Advanced Departmental Electives
Several advanced departmental electives are offered to deepen student expertise in specialized areas:
- Deep Learning: This course explores convolutional neural networks (CNNs), recurrent neural networks (RNNs), transformers, and generative adversarial networks (GANs). Students gain hands-on experience with frameworks like TensorFlow and PyTorch.
- Natural Language Processing: Covering topics such as sentiment analysis, machine translation, named entity recognition, and dialogue systems. Students work on real datasets to build language models capable of understanding context and generating human-like text.
- Computer Vision: Focuses on image processing techniques, object detection, facial recognition, and 3D reconstruction using deep learning approaches.
- Reinforcement Learning: Examines policy gradients, Q-learning, actor-critic methods, and multi-agent systems. Practical implementation involves building agents that learn optimal strategies through trial-and-error interactions.
- AI Ethics and Responsible Innovation: Addresses ethical dilemmas in AI deployment, algorithmic bias, privacy protection, and regulatory compliance. Students engage with case studies from real-world applications to understand the societal impact of AI decisions.
- Human-Computer Interaction: Studies user interface design principles, usability testing, and accessibility standards. This course emphasizes creating intuitive systems that enhance human performance through intelligent interaction.
- AI for Healthcare: Applies AI techniques to medical diagnostics, drug discovery, personalized treatment plans, and health data analysis. Students collaborate with healthcare professionals to develop solutions addressing critical medical challenges.
- Autonomous Systems: Focuses on robotics, navigation systems, control theory, and sensor fusion. Practical components include building self-driving vehicles or drones capable of autonomous decision-making.
- Quantum Machine Learning: Introduces quantum computing concepts and their integration with machine learning algorithms. Students explore quantum circuits, quantum algorithms, and hybrid classical-quantum systems for solving complex problems.
Project-Based Learning Philosophy
The department places significant emphasis on project-based learning to ensure that students apply theoretical knowledge in practical scenarios. This approach fosters creativity, collaboration, and critical thinking skills essential for success in AI research and industry roles.
Mini-projects are assigned during the third and fourth semesters to reinforce key concepts learned in core courses. These projects typically last 8-12 weeks and involve teams of 3-5 students working under faculty supervision. Each project has clear learning objectives, deliverables, and evaluation criteria.
The final-year thesis/capstone project is a substantial endeavor that spans the entire eighth semester. Students select a topic aligned with their interests or industry needs and work closely with a faculty advisor. The project culminates in a comprehensive report, presentation, and demonstration of the developed system or solution.
Project selection is facilitated through an online portal where students can browse available topics proposed by faculty members or submit their own ideas for approval. Faculty mentors are matched based on expertise and availability to ensure optimal guidance throughout the project lifecycle.