🌐 AI Course Mind Map – Chapters 1 to 7

1. 🧠 Chapter 1: Introduction to Artificial Intelligence Definition Simulation of human intelligence by machines Types of AI Narrow AI General AI Super AI History Turing Test Dartmouth Conference (1956) AI Winters Rise of Deep Learning Applications Healthcare Finance Robotics Education Challenges Bias Ethical issues Explainability Data dependency

2. 👨‍⚖️ Chapter 7: Expert Systems Components Knowledge Base Inference Engine User Interface Explanation Facility Types Rule-Based Frame-Based Hybrid Applications Medical Diagnosis (e.g., MYCIN) Legal Advice Technical Support Pros & Cons ✅ Fast, Consistent ❌ Lacks creativity, hard to update

3. 🤖 Chapter 2: Intelligent Agents What is an Agent System with sensors and actuators Types of Agents Simple Reflex Model-based Goal-based Utility-based Learning Agent Agent Structure Sensors → Percepts → Reasoning → Actuators Environment Types Fully / Partially Observable Deterministic / Stochastic Static / Dynamic Episodic / Sequential

4. 🧭 Chapter 3: Problem Solving and Search Techniques Problem Formulation Initial State Goal State Actions Cost Uninformed Search Breadth-First Search (BFS) Depth-First Search (DFS) Uniform Cost Search Informed Search (Heuristics) Greedy Best-First Search A* Search Adversarial Search Minimax Algorithm Alpha-Beta Pruning

5. 📘 Chapter 4: Knowledge Representation Types of Knowledge Declarative Procedural Meta-knowledge Representation Methods Propositional Logic First-Order Logic Semantic Networks Frames Production Rules Ontologies Inference Techniques Forward Chaining Backward Chaining

6. 🧪 Chapter 5: Machine Learning Types of ML Supervised Unsupervised Reinforcement Learning Algorithms Decision Trees KNN SVM Neural Networks Key Concepts Training vs Testing Overfitting / Underfitting Feature Selection Evaluation Metrics Accuracy Precision Recall F1-Score

7. 🗣️ Chapter 6: Natural Language Processing (NLP) Levels of NLP Syntax Semantics Pragmatics Core Techniques Tokenization Lemmatization / Stemming POS Tagging Named Entity Recognition (NER) Applications Chatbots Machine Translation Text Summarization Sentiment Analysis NLP Models TF-IDF Word2Vec Transformers (BERT, GPT)