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,基本方法与部分应用。 本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。

课程大纲

Week 1: Part I. Basics: Chapter 1. Introduction

In which we try to explain why we consider artificial intelligence to be a subject most worthy of study, and to decide what exactly it is.


Week 2: Part I. Basics: Chapter 2. Intelligent Agents

In which we discuss the nature of agents, the diversity of environments, and the resulting menagerie of agent types.


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

In which we see how an agent can find a sequence of actions that achieves its goals.


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

In which we relax the simplifying assumptions of the previous chapter, thereby getting closer to the real world.


Week 5: Part II. Searching: Chapter 5. Adversarial Search

In which we examine the problems that arise when we try to plan ahead in a world where other agents are planning against us.


Week 6: Part II. Searching: Chapter 6. Constraint Satisfaction Problem

In which we see how treating states as more than just little black boxes so that it leads to the invention of a range of powerful new search methods and a deeper understanding of problem structure and complexity.


Week 7: Part III. Reasoning: Chapter 7. Reasoning by Knowledge

In which we design agents that can form representations of a complex world, use a process of inference to derive new representations about the world.


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

In which we introduce a representation for classical planning problems, then for planning and acting in the real world.


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

In which we describe agents that can improve their behavior through learning of their own experiences, and discuss the perspectives on so many learning algorithms we have been faced with.


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

In which we discuss in detail about the tasks that can be solved with machine learning.


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

In which we discuss in detail about the paradigms that have been proposed in machine learning.


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

In which we discuss in detail about the models that have been used in machine learning.

课程说明

本课程采用双语教学,即英文PPT和作业等、中文讲授和交流。

参考资料

[1] 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。

[2] 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。

[3] Artificial Intelligence: A Modern Approach, http://aima.cs.berkeley.edu/

注:这是上述英文教材的网站,有许多相关的资源。

[4] Mehryar Mohri, Afshin Rostamizadeh and Ameet Talwalkar. "Foundations of Machine Learning". The MITPress, Aug. 17, 2012.

拓展阅读

其他

主讲教师

王文敏   

1989年3月取得哈工大计算机博士学位,毕业后任哈理工、哈工大副教授等。1992年出国并在日美知名公司担任主任研究员、主干研究员、中国区总工等。2009年底应邀回国,任北京大学数字视频编解码国家工程实验室广州中心副主任。2012年3月任北京大学信息工程学院常务副院长(主持工作),2013年9月至2016年3月任北京大学信息工程学院院长。主要研究领域:计算机视觉、多媒体检索、人工智能与机器学习。

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