Part I. Basics: Chapter 1. Introduction 1.1 Overview of Artificial Intelligence 1.2 Foundations of Artificial Intelligence 1.3 History of Artificial Intelligence 1.4 The State of The Art 1.5 Summary Quizzes for Chapter 1 Part I. Basics: Chapter 2. Intelligent Agent 2.1 Approaches for Artificial Intelligence 2.2 Rational Agents 2.3 Task Environments 2.4 Intelligent Agent Structure 2.5 Category of Intelligent Agents 2.6 Summary Quizzes for Chapter 2 Part II. Searching: Chapter 3. Solving Problems by Search 3.1 Problem Solving Agents 3.2 Example Problems 3.3 Searching for Solutions 3.4 Uninformed Search Strategies 3.5 Informed Search Strategies 3.6 Heuristic Functions 3.7 Summary Quizzes for Chapter 3 Part II. Searching: Chapter 4. Local Search and Swarm Intelligence 4.1 Overview 4.2 Local Search Algorithms 4.3 Optimization and Evolutionary Algorithms 4.4 Swarm Intelligence and Optimization 4.5 Summary Quizzes for Chapter 4 Part II. Searching: Chapter 5. Adversarial Search 5.1 Games 5.2 Optimal Decisions in Games 5.3 Alpha-Beta Pruning 5.4 Imperfect Real-time Decisions 5.5 Stochastic Games 5.6 Monte-Carlo Methods 5.7 Summary Quizzes for Chapter 5 Part II. Searching: Chapter 6. Constraint Satisfaction Problem 6.1 Constraint Satisfaction Problems (CSPs) 6.2 Constraint Propagation: Inference in CSPs 6.3 Backtracking Search for CSPs 6.4 Local Search for CSPs 6.5 The Structure of Problems 6.6 Summary Quizzes for Chapter 6 Part III. Reasoning: Chapter 7. Reasoning by Knowledge 7.1 Overview 7.2 Knowledge Representation 7.3 Representation using Logic 7.4 Ontological Engineering 7.5 Bayesian Networks 7.6 Summary Quizzes for Chapter 7 Part IV. Planning: Chapter 8. Classic and Real-world Planning 8.1 Planning Problems 8.2 Classic Planning 8.3 Planning and Scheduling 8.4 Real-World Planning 8.5 Decision-theoretic Planning 8.6 Summary Quizzes for Chapter 8 Part V. Learning: Chapter 9. Perspectives about Machine Leaning 9.1 What is Machine Learning 9.2 History of Machine Learning 9.3 Why Different Perspectives 9.4 Three Perspectives on Machine Learning 9.5 Applications and Terminologies 9.6 Summary Quizzes for Chapter 9 Part V. Learning: Chapter 10. Tasks in Machine Learning 10.1 Classification 10.2 Regression 10.3 Clustering 10.4 Ranking 10.5 Dimensionality Reduction 10.6 Summary Quizzes for Chapter 10 Part V. Learning: Chapter 11. Paradigms in Machine Learning 11.1 Supervised Learning Paradigm 11.2 Unsupervised Learning Paradigm 11.3 Reinforcement Learning Paradigm 11.4 Other Learning Paradigms 11.5 Summary Quizzes for Chapter 11 Part V. Learning: Chapter 12. Models in Machine Learning 12.1 Probabilistic Models 12.2 Geometric Models 12.3 Logical Models 12.4 Networked Models 12.5 Summary Quizzes for Chapter 12