Semester-wise syllabus for an M.Tech in Artificial Intelligence

Semester 1: Core Foundations 

Courses: 

1. Mathematics for AI

   - Linear algebra, probability, statistics, optimization, and calculus for machine learning. 

2. Machine Learning Fundamentals

   - Supervised/unsupervised learning, regression, SVM, decision trees, evaluation metrics. 

3. Python Programming for AI 

   - NumPy, Pandas, Scikit-learn, and data preprocessing techniques. 

4. Deep Learning Basics 

   - Neural networks, backpropagation, CNNs, and frameworks (TensorFlow/PyTorch). 

5. Research Methodology 

   - Technical writing, literature review, and ethics in AI. 

Labs: 

- Python Programming Lab (Jupyter, Colab) 

- Machine Learning Lab (Scikit-learn projects) 

Semester 2: Advanced AI & Electives

Core Courses: 

1. Advanced Deep Learning

   - RNNs, LSTMs, Transformers, GANs, and attention mechanisms. 

2. Natural Language Processing (NLP) 

   - Tokenization, embeddings, BERT, GPT, and Hugging Face libraries. 

Electives (Examples): 

- Computer Vision (OpenCV, YOLO, object detection) 

- Reinforcement Learning (Q-learning, policy gradients, OpenAI Gym) 

- AI for Robotics (SLAM, path planning, ROS integration) 

- Big Data Analytics (Spark, Hadoop, distributed ML) 

- AI Ethics and Fairness (bias detection, explainability, regulatory compliance) 

Labs: 

- Deep Learning Lab (TensorFlow/PyTorch projects) 

- NLP Lab (NLTK, spaCy, Transformer models) 

Semester 3: Specialization & Project Work 

Electives (Examples): 

- Generative AI (Diffusion models, LLMs, Stable Diffusion) 

- AI in Healthcare (Medical imaging, drug discovery) 

- Edge AI (TinyML, model optimization for IoT devices) 

- Quantum Machine Learning (Basics of quantum algorithms for AI) 

- AI for Cybersecurity (Anomaly detection, adversarial attacks) 

Project/Dissertation: 

- Phase 1: Topic selection (e.g., AI-driven chatbot, autonomous system, fraud detection), literature review, and proposal. 

- Seminars: Presentations on trends like multimodal AI, AI regulation, or AI-augmented creativity. 

Semester 4: Thesis/Project Completion 

Thesis/Project: 

- Full-time focus on implementation (e.g., training/deploying models, building AI systems). 

- Final documentation, viva voce defense, and deployment (cloud/edge). 

Additional Components:

- Industrial Internship (optional, with AI firms like NVIDIA, Google AI, or startups). 

- Workshops: Training in MLOps tools (MLflow, Kubeflow), AI deployment (Docker, Flask), or cloud platforms (AWS SageMaker, Azure ML). 

Elective Tracks (Specializations):

1. Computer Vision 

   - Image/video analysis, autonomous vehicles, AR/VR. 

2. NLP and Conversational AI 

   - Chatbots, sentiment analysis, multilingual models. 

3. AI Engineering 

   - MLOps, scalable AI systems, model deployment. 

4. AI for Social Good 

   - Climate modeling, healthcare accessibility, ethical AI.