*Semester 1: Foundation Basics* 

1. *Mathematics-I* (Calculus, Linear Algebra) 

2. *Programming Fundamentals* (Python, C/C++) 

3. *Introduction to AI* (History, Applications, Ethics) 

4. *Digital Logic Design* (Boolean Algebra, Circuits) 

5. *Communication Skills* 

6. *Environmental Science* 

*Lab*: Python Programming Lab, Basic Robotics (Arduino/Raspberry Pi) 

 

*Semester 2: Core Programming & Math* 

1. *Mathematics-II* (Probability, Statistics) 

2. *Data Structures & Algorithms* (Arrays, Trees, Graphs) 

3. *Object-Oriented Programming* (Java/C++) 

4. *Physics for AI* (Sensors, Actuators, IoT Basics) 

5. *Discrete Mathematics* (Logic, Combinatorics) 

*Lab*: Algorithm Implementation, Sensor Data Collection 

 

*Semester 3: AI Fundamentals* 

1. *Mathematics-III* (Multivariate Calculus, Optimization) 

2. *Introduction to Machine Learning* (Regression, Classification) 

3. *Database Systems* (SQL, NoSQL, MongoDB) 

4. *Computer Organization* (CPU, GPU Architecture) 

5. *Signals and Systems* (Time/Frequency Domain) 

*Lab*: ML with Scikit-learn, SQL/NoSQL Projects 

 

*Semester 4: Machine Learning & Robotics* 

1. *Linear Algebra for AI* (Matrix Operations, Eigenvectors) 

2. *Deep Learning Basics* (Neural Networks, TensorFlow/PyTorch) 

3. *Robotics Fundamentals* (Kinematics, Sensors, ROS) 

4. *Operating Systems* (Process Management, Threading) 

5. *Ethics in AI* (Bias, Fairness, GDPR) 

*Lab*: Neural Network Implementation, ROS Simulations 

 

*Semester 5: Advanced AI & Applications* 

1. *Natural Language Processing* (NLTK, Transformers, BERT) 

2. *Computer Vision* (OpenCV, CNNs, YOLO) 

3. *Reinforcement Learning* (Q-Learning, Policy Gradients) 

4. *Cloud Computing* (AWS/Azure, AI Model Deployment) 

5. *Elective-I* (e.g., *Generative AI*: GANs, Diffusion Models) 

*Lab*: NLP Chatbots, Object Detection Projects 

 

*Semester 6: AI Systems & Specialization* 

1. *AI for Robotics* (SLAM, Path Planning) 

2. *Big Data Analytics* (Spark, Hadoop, Kafka) 

3. *Distributed AI Systems* (Edge AI, Federated Learning) 

4. *AI in Healthcare/Finance* (Case Studies) 

5. *Elective-II* (e.g., *Autonomous Vehicles*: Perception, Control) 

*Lab*: Edge AI Deployment, Industry-Specific AI Projects 

 

*Semester 7: Capstone Projects & Research* 

1. *AI System Design* (MLOps, CI/CD Pipelines) 

2. *Advanced Topics* (Quantum Machine Learning, AI Safety) 

3. *Elective-III* (e.g., *AI for Cybersecurity*) 

4. *Elective-IV* (e.g., *AI in Gaming*: Unity ML-Agents) 

5. *Capstone Project-I* (Industry Problem, e.g., Predictive Maintenance) 

*Lab*: Full-Stack AI Solutions, Ethical AI Audits 

 

 

*Semester 8: Industry Integration* 

1. *Industrial Internship* (6–8 Weeks at AI Labs/Startups) 

2. *Capstone Project-II* (Thesis: e.g., AI-Driven Drug Discovery) 

3. *Professional Ethics* 

4. *Elective-V* (e.g., *AI Policy & Regulation*) 

 

*Electives (Sample)* 

- *AI for Climate Science* 

- *Swarm Intelligence* 

- *AI in Agriculture* 

- *Neuromorphic Computing* 

- *AI-Driven Art & Creativity*