课程概况
人工智能是国内外著名大学计算机专业设置的骨干课之一，也是国内外著名高校和研究机构的主要研究方向之一。人工智能研究如何用计算机软件和硬件去实现Agent的感知、决策与智能行为，其理论基础表现为搜索、推理、规划和学习，应用领域包括计算机视觉、图像分析、模式识别、专家系统、自动规划、智能搜索、计算机博弈、智能控制、机器人学、自然语言处理、社交网络、数据挖掘、虚拟现实等。
本课程在系统回顾人工智能发展历程的基础上，重点介绍人工智能的核心思想、基本理论，基本方法与部分应用。 课程以该英文原版教材为主，并根据人工智能、特别是机器学习领域的发展和变化，编撰和充实了大量的内容。本课程共有12讲，采用双语教学，即中英文PPT和中英文作业等、中文讲授和交流。
课程大纲
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
预备知识
学习者具备一定的数据结构、算法等计算机知识，线性代数、概率论等数学知识，以及机器学习的基础知识即可。
参考资料
[1] 王文敏：《人工智能原理》，高等教育出版社，2019年8月28日
https://www.hep.com.cn/book/details?uuid=b0900ec1-16ac-1000-804d-4f70147a3a5d
[2] Stuart Russell, Peter Norvig. "Artificial Intelligence: A Modern Approach (3rd Edition)". Prentice Hall, Dec. 11, 2009.
注：这本书被认为是最经典的人工智能教材，已被全球100多个国家的1200多所著名大学选用。
[3] Stuart Russell等著，殷建平等译：《人工智能：一种现代的方法 (第3版)》，清华大学出版社，2013年11月1日。
注：这本书是上述英文教材的中译本，我国已将其作为“世界著名计算机教材精选”之一。
[4] Artificial Intelligence: A Modern Approach, https://aima.cs.berkeley.edu/
注：这是上述英文教材的网站，有许多相关的资源。
[5] Mehryar Mohri, Afshin Rostamizadeh and Ameet Talwalkar. "Foundations of Machine Learning". The MITPress, Aug. 17, 2012.
