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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