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人工智能原理
人工智能原理
20万 人选课
更新日期:2022/11/12
开课平台 爱课程(中国大学mooc)
开课高校 北京大学
开课教师 王文敏
学科专业 工学 计算机类
开课时间 2022/09/05 - 2022/12/25
课程周期 16 周
开课状态 开课中
每周学时 -
课程简介

        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

quizzes for chapter 6

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

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

quizzes for chapter 8

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

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

quizzes for chapter 11

11.1 supervised learning paradigm

11.2 unsupervised learning paradigm

11.3 reinforcement learning paradigm

11.4 other learning paradigms

11.5 summary

part v. learning: chapter 12. models in machine learning

quizzes for chapter 12

12.1 probabilistic models

12.2 geometric models

12.3 logical models

12.4 networked models

12.5 summary