Module 1 – Foundations of AI 

• Definition and history of AI
• Goals and types of AI (Narrow, General, Super AI)
• AI vs. Machine Learning vs. Deep Learning
• Applications of AI across industries
• Practical:
• Setting up Python + Jupyter + libraries (NumPy, Pandas)
• Introduction to Python

Module 2 – Problem Solving and Search 

• State-space search
• Uninformed search (BFS, DFS)
• Informed search (A*, Greedy search)
• Practical:
  • Implement BFS and DFS in Python
  • Simple puzzle solving (8-puzzle, pathfinding)

Module 3 – Knowledge Representation and Reasoning 

• Propositional & First Order Logic
• Rule-based expert systems
• Ontologies
• Practical:
  • Build a basic rule-based expert system

Module 4 – Machine Learning Basics 

• Supervised, Unsupervised, Reinforcement Learning
• Supervised Learning Algorithms- Linear Regression, Logistic Regression, Decision Trees, k-NN
• Evaluation metrics
• Unsupervised Learning Algorithms – K Means Clustering, Hierarchical Clustering
• Practical:
  • Hands-on with sklearn: Regression, Classification, Clustering
  • Train/Test split, Accuracy evaluation

Module 5 – Deep Learning Essentials 

• Neural Networks basics
• Activation functions, Backpropagation
• Introduction to CNNs and RNNs
• Practical:
  • Build a simple neural network using TensorFlow/PyTorch
  • Image classification with MNIST dataset

Module 6 – Natural Language Processing 

• NLP tasks- Tokenization, Stop Word Removal, Stemming, Lemmatization, NER
• Text classification, Sentiment analysis
• Word embeddings (Word2Vec, GloVe basics)
• Practical:
  • Sentiment analysis using NLTK or Hugging Face transformers
  • Simple Chatbot Prototype

Module 7 – AI Ethics and Responsible AI 

• Bias, fairness, privacy, explainability
• Responsible AI frameworks
• Practical:
  • Case studies discussion
  • Analyze fairness in a small dataset

Module 8 – Applications, Trends & Project Work 

• AI in healthcare, finance, transportation
• Emerging trends- Generative AI, LLMs, Autonomous Systems
• Practical:
  • Capstone project: Students choose an AI mini-project
  • Presentations and evaluations