Artificial Intelligence

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Stuart Russell was born in 1962 in Portsmouth, England. He received his B.A. with first-class honours in physics from Oxford University in 1982, and his Ph.D. in computer science from Stanford in 1986. He then joined the faculty of the University of California at Berkeley, where he is a professor of computer science, director of the Center for Intelligent Systems, and holder of the Smith–Zadeh Chair in Engineering. In 1990, he received the Presidential Young Investigator Award of the National Science Foundation, and in 1995 he was cowinner of the Computers and Thought Award. He was a 1996 Miller Professor of the University of California and was appointed to a Chancellor’s Professorship in 2000. In 1998, he gave the Forsythe Memorial Lectures at Stanford University. He is a Fellow and former Executive Council member of the American Association for Artificial Intelligence. He has published over 100 papers on a wide range of topics in artificial intelligence. His other books include The Use of Knowledge in Analogy and Induction and (with Eric Wefald) Do the Right Thing: Studies in Limited Rationality.

Peter Norvig is currently Director of Research at Google, Inc., and was the director responsible for the core Web search algorithms from 2002 to 2005. He is a Fellow of the American Association for Artificial Intelligence and the Association for Computing Machinery. Previously, he was head of the Computational Sciences Division at NASA Ames Research Center, where he oversaw NASA’s research and development in artificial intelligence and robotics, and chief scientist at Junglee, where he helped develop one of the first Internet information extraction services. He received a B.S. in applied mathematics from Brown University and a Ph.D. in computer science from the University of California at Berkeley. He received the Distinguished Alumni and Engineering Innovation awards from Berkeley and the Exceptional Achievement Medal from NASA. He has been a professor at the University of Southern California and a research faculty member at Berkeley. His other books are Paradigms of AI Programming: Case Studies in Common Lisp and Verbmobil: A Translation System for Face-to-Face Dialog and Intelligent Help Systems for UNIX.

出版者:Pearson
作者:Stuart Russell
出品人:
頁數:1152
译者:
出版時間:2009-12-11
價格:USD 195.00
裝幀:Hardcover
isbn號碼:9780136042594
叢書系列:
圖書標籤:
  • 人工智能 
  • AI 
  • Artificial.Intelligence 
  • 計算機 
  • 計算機科學 
  • CS 
  • 算法 
  • 機器學習 
  •  
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The long-anticipated revision of this #1 selling book offers the most comprehensive, state of the art introduction to the theory and practice of artificial intelligence for modern applications. Intelligent Agents. Solving Problems by Searching. Informed Search Methods. Game Playing. Agents that Reason Logically. First-order Logic. Building a Knowledge Base. Inference in First-Order Logic. Logical Reasoning Systems. Practical Planning. Planning and Acting. Uncertainty. Probabilistic Reasoning Systems. Making Simple Decisions. Making Complex Decisions. Learning from Observations. Learning with Neural Networks. Reinforcement Learning. Knowledge in Learning. Agents that Communicate. Practical Communication in English. Perception. Robotics. For computer professionals, linguists, and cognitive scientists interested in artificial intelligence.

具體描述

讀後感

評分

这本书居然04年就出了,而且出了中文版。为什么我那时就没有找到这本书呢?不然现在的我可能就不是今天的我。 当然,一个很大的问题是:那时的我看了这本书以后能够看得懂吗?就算那时我可以解除到这本书,那时的我到底会怎样的对待呢?  

評分

涵容量大,内容较深,但是涉及很多深刻的内容的时候,解释之精炼明了又让人拍案叫绝。无论如何,这本书还是不适合初学人工智能的读者阅读,而适合有一定AI基础,希望有进一步认识的读者阅读。因此,一般作为计算机科学系的研究生课程教材。  

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疯狂的书~~~ 深度和广度俱佳, 理论讲解透彻,实践细节准确, 语言通俗,对人工智能的归类和划分有启发意义。  

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一样的清新醒目,一样的幽默风趣,Monty Python里的大段搞笑台词,故事书风格的"in which"章节概要。不同的是时下统计大热,第二版也增加了大量的统计推断和学习理论。只是同专门论述统计学习的书籍比起来,篇幅所限,跳跃还是太快,讲解也只能从简。不过,我一直最佩服Peter N...  

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为什么还没有翻译成中文呢? 英文看起来有点小困难,什么时候才出汉译版。 这个评论说太短了,下面将执行复制代码。 这个评论说太短了,下面将执行复制代码。 这个评论说太短了,下面将执行复制代码。 这个评论说太短了,下面将执行复制代码。 这个评论说太短了,下面将执行复...  

用戶評價

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The best AI intro-- search, logic, and machine learning-- creating an AI needs a generalist leading three major teams if specialists. Highly recommend.

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經典,前後讀瞭2遍,前半部分比較難,後半部分比較簡單

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tag機器學習的是什麼心態?!內容選擇和邏輯都挺好的 就是排版有點抓不住重點

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看著混個臉熟

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這學期的教材,讀瞭大概50-60%的內容。大而全,優點是麵麵俱到,缺點是無法dive into details. 脈絡還是比較清晰的,從Agent齣發,探討不同的問題和子領域。像Search, Adversarial Search和Planning這幾章寫的就很有味道。但是一些比較偏計算的內容如HMM,顯然不夠精細,有時候也有冗文之嫌,這些地方還是建議參考其他材料。總的來說是Best Introduction to AI as a whole.

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