综合_计算机基础理论_计算机类_最新资料_得益网

Computation Engineering: Applied Automata Theory and Logic

2008-11-14 08:20:34

Review

From the reviews:

"One of the constant challenges faced by computer science faculty is how to tie in the theory of computing with applications. This text attempts to do just that … . Overall, this book is a good undergraduate theory text. … It includes exercises, with software tools to aid in visualization of key ideas. … The exercises are appropriate for an undergraduate-level class. … It is a good text … ." (M. D. Derk, Computing Reviews, December, 2006)

Product Description

The computer hardware and software industry is committed to using formal methods. As a result, it is crucial that students who take automata theory and logic courses retain what they have learned and understand how to use their knowledge. Yet many textbooks typically emphasize automata theory only, not logic, thus losing a valuable opportunity to tie these subjects together and reinforce learning. In fact, automata theory and logic evolved hand-in-hand, yet this connection was severed in the '70s as separate automata-theory and logic courses became possible. Now, with computer science departments suffering from overcrowded syllabi, it is often possible for undergraduates to get a BS without having had to take a course in mathematical logic!

Today's students want to know how knowledge can work for them -- learning theory as a tool is preferable to learning theory for theory's sake. To prove that theoretical tenents are not only applicable, but also necessary and relevant, useful examples must be presented. This textbook uses interactive tools throughout, such as simple BDD and SAT tools. By providing a blend of theory and practical applications the material is shown to be both inviting and current. Topics are also illustrated in multiple domains so that information is reinforced and students can begin to tie automata theory and logic together. They will also learn multiple uses of fixed-points, including BDD based model checking and understanding context-free productions.

Having used this book, students will not only know and understand automata theory, but also be able to apply their knowledge in real practice.

图和网络与算法

2008-11-03 07:50:46

第一篇图论
第一章基本概念
第二章树、割集和回路
第三章欧拉图和哈密顿图
第四章图和矢量空间
第五章有向图
第六章图的矩阵
第七章平面性和对偶性
第八章连通度和匹配
第九章覆盖和着色
第十章拟阵
第二篇电网络理论
第十一章图和网络
第十二章 N端口电阻网络
第十三章网络函数和网络灵敏度
第三篇算法图论
第十四章算法分析
第十五章算法优化
名词索引

中大课程录象概率统计51

2008-10-23 14:57:06

《机器学习》英文版

2008-10-23 00:47:15

内容简介
　　机器学习这门学科研究的是能通过经验自动改进的计算机算法，其应用从数据挖掘程序到信息过滤系统，再到自动机工具，已经非常丰富。机器学习从很多学科吸收了成果和概念，包括人工智能、概论论与数理统计、哲学、信息论、生物学、认知科学和控制论等，并以此来理解问题的背景、算法和算法中的隐含假定。
　　本书展示了机器学习中的核心算法和理论，并阐明了算法的过行过程。书中主要涵盖了目前机器学习中各种最实用的理论和算法，包括概念学习、决策树、神经网络、贝叶斯学习、基于实例的学习、遗传算法、规则学习、基于解释的学习和增强学习等。对每一个主题，作者不仅进行了十分详尽和直观的解释，还给出了实用的算法流程。本书被卡内基梅隆等许多大学作为机器学习课程的教材。机器学习这门学科研究的是能通过经验自动改进的计算机算法，其应用从数据挖掘程序到信息过滤系统，再到自动机工具，已经非常丰富。机器学习从很多学科吸收了成果和概念，包括人工智能、概论论与数理统计、哲学、信息论、生物学、认知科学和控制论等，并以此来理解问题的背景、算法和算法中的隐含假定。

作者简介
　　Tom M.Mitchell是卡内基梅隆大学教授，目前担任该校自动学习和发现中心主任。他还是美国人工智能协会的主席，并且是《Machine Learning》杂志和国际机器学习会议的创办者。

目录
1 Introduction
2 Concept Learning and the General-to-Specific Ordering
3 Decision Tree Learning
4 Artificial Neural Networks
5 Evaluating Hypotheses
6 Bayesian Learning
7 Computational Learning Theory
8 Instance-Based Learning
9 Genetic Algorithms
10 Learning Sets Rules
11 Analytical Learning
12 Combining Inductive and Analytical Learning
13 Reinforcement Learning
Appendix

中大课程录象概率统计50

2008-10-22 10:36:23

中大课程录象概率统计49

2008-10-22 10:36:21

中大课程录象概率统计48

2008-10-22 10:36:15

中大课程录象概率统计48

2008-10-22 10:31:41

人工智能:一种现代的方法(第二版)英文版

2008-10-22 02:43:43

本书被全世界89个国家的900多所大学用作教材。
本书以详尽和丰富的资料，从理性智能体的角度，全面阐述了人工智能领域的核心内容，并深入介绍了各个主要的研究方向。全书分为8大部分：第一部分“人工智能”，第二部分“问题求解”，第三部分“知识与推理”，第四部分“规划”，第五部分“不确定知识与推理”，第六部分“学习”，第七部分“通信、感知与行动 ”，第八部分“结论”。本书既详细介绍了人工智能的基本概念、思想和算法，还描述了其各个研究方向最前沿的进展，同时收集整理了详实的历史文献与事件。另外，本书的配套网址http://aima.cs.berkeley.edu/为教师和学生提供了大量教学和学习资料。
本书适合于不同层次和领域的研究人员及学生，是高等院校本科生和研究生人工智能课的首选教材，也是相关领域的科研与工程技术人员的重要参考书。

作者简介
Stuart Russell,was born in 1962 in Portsmouth,England.He received his B.A.with first-class hon-ours in physics from Oxford Undiversity 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 1996Miller Professor of the University of California and was appointed to a Chancellor s Professor ship 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.

目录
I Artificial Intelligence
1 Introduction
2 IntelligentAgents
11 Problem-solving
3 Solving Problems by Searching
4 Informed Search and Exploration
5 Constraint Satisfaction Problems
6 Adversarial Search
Ill Knowledge and reasoning
7 Logical Agents
8 First-Order Logic
9 Inference in First-Order Logic
10 Knowledge Representation
IV Planning
11 Planning
12 Planning and Acting in the Real WOrld
V Uncertain knowledge and reasoning
13 Uncertainty
14 Probabilistic Reasoning
15 Probabilistic Reasoning over Time
16 Making Simple Decisions
17 Making Complex Decisions
VI Learning
18 Learning from Observations
19 Knowledge in Learning
20 Statistical Learning Methods
21 Reinforcement Learning
Vll Communicating, perceiving, and acting
22 Communication
23 Probabilistic Language Processing
24 Perception
25 Robottes
Vlll ConCluSionS
26 Philosophical Foundations
27 Al: Present and Future
A Mathematical background
B Notes on Languages and Algorithms.
Bibliography
Index

中大课程录象概率统计47

2008-10-21 10:07:09

中大课程录象概率统计46

2008-10-21 10:07:04

中大课程录象概率统计45

2008-10-21 10:07:00

中大课程录象概率统计44

2008-10-21 10:06:56

中大课程录象概率统计43

2008-10-21 10:06:51

中大课程录象概率统计42

2008-10-21 10:06:47

中大课程录象概率统计41

2008-10-21 10:06:42

中大课程录象概率统计40

2008-10-21 09:49:13

中大课程录象概率统计39

2008-10-21 09:49:09

中大课程录象概率统计38

2008-10-21 09:48:42

中大课程录象概率统计37

2008-10-21 09:47:49

中大课程录象概率统计36

2008-10-21 09:47:38

中大课程录象概率统计35

2008-10-21 09:45:56

中大课程录象概率统计35

2008-10-20 15:13:36

中大课程录象概率统计34

2008-10-17 16:52:27

中大课程录象概率统计33

2008-10-17 16:52:24

中大课程录象概率统计32

2008-10-17 16:52:21

中大课程录象概率统计31

2008-10-17 16:52:19

中大课程录象概率统计30

2008-10-17 16:52:16

中大课程录象概率统计29

2008-10-17 16:52:13

中大课程录象概率统计28

2008-10-17 16:52:11

中大课程录象概率统计27

2008-10-17 16:52:07

中大课程录象概率统计26

2008-10-17 16:41:11

中大课程录象概率统计25

2008-10-17 16:41:08

中大课程录象概率统计24

2008-10-17 16:41:04

中大课程录象概率统计23

2008-10-17 16:41:00

中大课程录象概率统计22

2008-10-17 16:40:57

中大课程录象概率统计21

2008-10-17 16:40:56

中大课程录象概率统计20

2008-10-17 16:40:56

中大课程录象概率统计19

2008-10-17 16:40:55

中大课程录象概率统计18

2008-10-15 14:42:58

中大课程录象概率统计17

2008-10-15 14:42:57

中大课程录象概率统计16

2008-10-15 14:42:55

中大课程录象概率统计15

2008-10-15 14:42:54

中大课程录象概率统计14

2008-10-15 14:42:52

中大课程录象概率统计13

2008-10-15 14:42:46

中大课程录象概率统计12

2008-10-14 10:41:25

中大课程录象概率统计11

2008-10-14 10:41:22

中大课程录象概率统计10

2008-10-14 10:41:13

中大课程录象概率统计09

2008-10-13 15:03:06

中大课程录象概率统计08

2008-10-13 15:03:05