Artificial Intelligence Tutorial: A Complete Guide
A comprehensive guide to Artificial Intelligence — from foundational concepts and history to advanced topics like knowledge representation, expert systems, and industry applications. Each chapter includes explanations, pseudocode, diagrams, and hands-on exercises.
Table of Contents
- 1. Introduction to AI Overview, Goals, Approaches, Applications
- 2. History & Evolution Timeline, Key Milestones, AI Winters, Modern AI
- 3. Types of AI Narrow AI, General AI, Super AI, Reactive, Limited Memory, Theory of Mind, Self-Aware
- 4. AI Terminology Key Terms: Agent, Percept, Heuristic, Overfitting, etc.
- 5. Tools & Frameworks TensorFlow, PyTorch, scikit-learn, Keras, JAX, Hugging Face
- 6. Applications & Real-Life Examples Healthcare, Finance, Transportation, Entertainment, Education
- 7. Ethics & Fairness Bias, Fairness, Transparency, Privacy, Accountability, Regulation
- 8. Challenges & Limitations Data Issues, Explainability, Robustness, Scalability, Common Pitfalls
- 9. Branches of AI ML, NLP, Computer Vision, Robotics, Neural Networks, Fuzzy Logic, Evolutionary Computation, Swarm Intelligence, Cognitive Computing
- 10. Intelligent Systems Concepts, Components, Types, Characteristics
- 11. Agents & Environments Agent Models, Environment Types, Interaction, PEAS Framework
- 12. Problem Solving Search Algorithms, CSPs, BFS, DFS, A*, Heuristics
- 13. Knowledge Representation Logic, Inference, FOL, Unification, Resolution, Chaining
- 14. Expert Systems Concepts, Architecture, Applications, Advantages & Limitations
- 15. AI in Practice Predictive Analytics, Personalization, Healthcare, Autonomous Vehicles
- 16. AI by Industry Manufacturing, Banking, Healthcare, Marketing, Automotive, Data Analytics
- 17. Advanced Topics Research Areas, Cognitive Computing, AI Tools, Frontier Models
- 18. Practice & Resources Interview Questions, Study Plan, Bootcamp, Certification
- 19. References Glossary, Cheatsheet, Useful Resources