人工智能原理 · 课程详情

人工智能原理

北京大学 · 大学MOOC · 共 67 节

中国工程院院士、中国人工智能学会理事长李德毅说，“人工智能在奔跑，平均每人拥有一台服务机器人的时代终将到来。”那么，究竟什么是人工智能？人工智能的理论基础是什么？其最新的发展与应用会如何影响我们的社会、工作和生活？本课程就将系统介绍人工智能的发展历程、核心思想、基本理论与应用。

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课程目录

Part I. Basics: Chapter 1. Introduction

1.1 Overview of Artificial Intelligence 播放 1.1 Overview of Artificial Intelligence 播放 1.2 Foundations of Artificial Intelligence 播放 1.3 History of Artificial Intelligence 播放 1.4 The State of The Art 播放

Part I. Basics: Chapter 2. Intelligent Agent

2.1 Approaches for Artificial Intelligence 播放 2.2 Rational Agents 播放 2.3 Task Environments 播放 2.4 Intelligent Agent Structure 播放 2.5 Category of Intelligent Agents 播放

Part II. Searching: Chapter 3. Solving Problems by Search

3.1 Problem Solving Agents 播放 3.2 Example Problems 播放 3.3 Searching for Solutions 播放 3.4 Uninformed Search Strategies 播放 3.4 Uninformed Search Strategies 播放 3.4 Uninformed Search Strategies 播放 3.4 Uninformed Search Strategies 播放 3.4 Uninformed Search Strategies 播放 3.4 Uninformed Search Strategies 播放 3.5 Informed Search Strategies 播放 3.5 Informed Search Strategies 播放 3.6 Heuristic Functions 播放

Part II. Searching: Chapter 4. Local Search and Swarm Intelligence

4.1 Overview 播放 4.2 Local Search Algorithms 播放 4.2 Local Search Algorithms 播放 4.2 Local Search Algorithms 播放 4.3 Optimization and Evolutionary Algorithms 播放 4.4 Swarm Intelligence and Optimization 播放

Part II. Searching: Chapter 5. Adversarial Search

5.1 Games 播放 5.2 Optimal Decisions in Games 播放 5.3 Alpha-Beta Pruning 播放 5.4 Imperfect Real-time Decisions 播放 5.5 Stochastic Games 播放 5.6 Monte-Carlo Methods 播放

Part II. Searching: Chapter 6. Constraint Satisfaction Problem

6.1 Constraint Satisfaction Problems (CSPs) 播放 6.2 Constraint Propagation: Inference in CSPs 播放 6.3 Backtracking Search for CSPs 播放 6.4 Local Search for CSPs 播放 6.5 The Structure of Problems 播放

Part III. Reasoning: Chapter 7. Reasoning by Knowledge

7.1 Overview 播放 7.2 Knowledge Representation 播放 7.3 Representation using Logic 播放 7.4 Ontological Engineering 播放 7.5 Bayesian Networks 播放

Part IV. Planning: Chapter 8. Classic and Real-world Planning

8.1 Planning Problems 播放 8.2 Classic Planning 播放 8.3 Planning and Scheduling 播放 8.4 Real-World Planning 播放 8.5 Decision-theoretic Planning 播放

Part V. Learning: Chapter 9. Perspectives about Machine Leaning

9.1 What is Machine Learning 播放 9.2 History of Machine Learning 播放 9.3 Why Different Perspectives 播放 9.4 Three Perspectives on Machine Learning 播放 9.5 Applications and Terminologies 播放

Part V. Learning: Chapter 10. Tasks in Machine Learning

10.1 Classification 播放 10.2 Regression 播放 10.3 Clustering 播放 10.4 Ranking 播放 10.5 Dimensionality Reduction 播放

Part V. Learning: Chapter 11. Paradigms in Machine Learning

11.1 Supervised Learning Paradigm 播放 11.2 Unsupervised Learning Paradigm 播放 11.3 Reinforcement Learning Paradigm 播放 11.4 Other Learning Paradigms 播放

Part V. Learning: Chapter 12. Models in Machine Learning

12.1 Probabilistic Models 播放 12.2 Geometric Models 播放 12.3 Logical Models 播放 12.4 Networked Models 播放

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