Computer Science: Books

Artificial intelligence : a modern approach by Stuart Russell; Peter Norvig; Stuart J. Russell 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. KEY TOPICS: 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. MARKET: For computer professionals, linguists, and cognitive scientists interested in artificial intelligence.

Computer Security: a hands-on approach by Wenliang Du This book is a part of another book titled "Computer & Internet Security: A Hands-on Approach, 2nd Edition" (687 pages). It is created to lower the cost on students, as many courses only need this part. It covers 17 of the 26 chapters. This book is based on the author's 18 years of teaching and research experience. It covers the fundamental principles in Computer Security, including software security, hardware security, web security, and cryptography. Its goal is to help readers understand how various attacks work, what their fundamental causes are, how to defend against them, and how various defense mechanisms work. The book can be used as a textbook for undergraduate and graduate courses. The author strongly believes in "learning by doing", so the book takes a hands-on approach. For each security principle, the book uses a series of hands-on activities to help explain the principle; readers can "touch", play with, and experiment with the principle, instead of just reading about it. For instance, if a security principle involves an attack, the book guides readers to actually launch the attack (in a contained environment). Readers can learn better from such hands-on activities. The hands-on activities are based on the author's widely adopted SEED Labs, which have been used by over 1000 institutes in more than 65 countries.

The art of computer programming - Seminumerical Algorithms by Donald E. Knuth The bible of all fundamental algorithms and the work that taught many of today's software developers most of what they know about computer programming.   -Byte, September 1995   I can't begin to tell you how many pleasurable hours of study and recreation they have afforded me! I have pored over them in cars, restaurants, at work, at home... and even at a Little League game when my son wasn't in the line-up.   -Charles Long   If you think you're a really good programmer... read [Knuth's] Art of Computer Programming... You should definitely send me a resume if you can read the whole thing.   -Bill Gates   It's always a pleasure when a problem is hard enough that you have to get the Knuths off the shelf. I find that merely opening one has a very useful terrorizing effect on computers.   -Jonathan Laventhol   The second volume offers a complete introduction to the field of seminumerical algorithms, with separate chapters on random numbers and arithmetic. The book summarizes the major paradigms and basic theory of such algorithms, thereby providing a comprehensive interface between computer programming and numerical analysis. Particularly noteworthy in this third edition is Knuth's new treatment of random number generators, and his discussion of calculations with formal power series. Ebook (PDF version) produced by Mathematical Sciences Publishers (MSP),http://msp.org

Introduction to Machine Learning by Ethem Alpaydin Introduction -- Supervised learning -- Bayesian decision theory -- Parametric methods -- Multivariate methods -- Dimensionality reduction -- Clustering -- Nonparametric methods -- Decision trees -- Linear discrimination -- Multilayer perceptrons -- Local models -- Kernel machines -- Graphical models -- Brief contents -- Hidden markov models -- Bayesian estimation -- Combining multiple learners -- Reinforcement learning -- Design and analysis of machine learning experiments.

Computational complexity : a modern approach by Sanjeev Arora; Boaz Barak This beginning graduate textbook describes both recent achievements and classical results of computational complexity theory. Requiring essentially no background apart from mathematical maturity, the book can be used as a reference for self-study for anyone interested in complexity, including physicists, mathematicians, and other scientists, as well as a textbook for a variety of courses and seminars. More than 300 exercises are included with a selected hint set. The book starts with a broad introduction to the field and progresses to advanced results. Contents include: definition of Turing machines and basic time and space complexity classes, probabilistic algorithms, interactive proofs, cryptography, quantum computation, lower bounds for concrete computational models (decision trees, communication complexity, constant depth, algebraic and monotone circuits, proof complexity), average-case complexity and hardness amplification, derandomization and pseudorandom constructions, and the PCP theorem.

Pattern Recognition and Machine Learning by Christopher M. Bishop This is the first textbook on pattern recognition to present the Bayesian viewpoint. The book presents approximate inference algorithms that permit fast approximate answers in situations where exact answers are not feasible. It uses graphical models to describe probability distributions when no other books apply graphical models to machine learning. No previous knowledge of pattern recognition or machine learning concepts is assumed. Familiarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.

Convex Optimization by Stephen Boyd; Lieven Vandenberghe Convex optimization problems arise frequently in many different fields. This book provides a comprehensive introduction to the subject, and shows in detail how such problems can be solved numerically with great efficiency. The book begins with the basic elements of convex sets and functions, and then describes various classes of convex optimization problems. Duality and approximation techniques are then covered, as are statistical estimation techniques. Various geometrical problems are then presented, and there is detailed discussion of unconstrained and constrained minimization problems, and interior-point methods. The focus of the book is on recognizing convex optimization problems and then finding the most appropriate technique for solving them. It contains many worked examples and homework exercises and will appeal to students, researchers and practitioners in fields such as engineering, computer science, mathematics, statistics, finance and economics.

The C Answer Book by Clovis L. Tondo; Scott E. Gimpel Provides solutions to all exercises in Kernighan & Ritchie's new ANSI C book. Ideal for use with K&R in any course on C. Careful study of this answer book will help understand ANSI C and enhance programming skills. Tondo & Gimpel describe each solution and completely format programs to show the logical flow.

Natural Language Processing with Python by Steven Bird; Ewan Klein; Edward Loper This book offers a highly accessible introduction to natural language processing, the field that supports a variety of language technologies, from predictive text and email filtering to automatic summarization and translation. With it, you'll learn how to write Python programs that work with large collections of unstructured text. You'll access richly annotated datasets using a comprehensive range of linguistic data structures, and you'll understand the main algorithms for analyzing the content and structure of written communication. Packed with examples and exercises, Natural Language Processing with Python will help you: Extract information from unstructured text, either to guess the topic or identify "named entities" Analyze linguistic structure in text, including parsing and semantic analysis Access popular linguistic databases, including WordNet and treebanks Integrate techniques drawn from fields as diverse as linguistics and artificial intelligence This book will help you gain practical skills in natural language processing using the Python programming language and the Natural Language Toolkit (NLTK) open source library. If you're interested in developing web applications, analyzing multilingual news sources, or documenting endangered languages -- or if you're simply curious to have a programmer's perspective on how human language works -- you'll find Natural Language Processing with Python both fascinating and immensely useful.

Principles of Data Mining by Max Bramer Data Mining, the automatic extraction of implicit and potentially useful information from data, is increasingly used in commercial, scientific and other application areas. Principles of Data Mining explains and explores the principal techniques of Data Mining: for classification, association rule mining and clustering. Each topic is clearly explained and illustrated by detailed worked examples, with a focus on algorithms rather than mathematical formalism. It is written for readers without a strong background in mathematics or statistics, and any formulae used are explained in detail. This second edition has been expanded to include additional chapters on using frequent pattern trees for Association Rule Mining, comparing classifiers, ensemble classification and dealing with very large volumes of data. Principles of Data Mining aims to help general readers develop the necessary understanding of what is inside the 'black box' so they can use commercial data mining packages discriminatingly, as well as enabling advanced readers or academic researchers to understand or contribute to future technical advances in the field. Suitable as a textbook to support courses at undergraduate or postgraduate levels in a wide range of subjects including Computer Science, Business Studies, Marketing, Artificial Intelligence, Bioinformatics and Forensic Science.

Dive into Python 3 by Mark Pilgrim Mark Pilgrim's Dive Into Python 3 is a hands-on guide to Python 3 and its differences from Python 2. As in the original book, Dive Into Python, each chapter starts with a real, complete code sample, proceeds to pick it apart and explain the pieces, and then puts it all back together in a summary at the end. This book includes: Example programs completely rewritten to illustrate powerful new concepts now available in Python 3: sets, iterators, generators, closures, comprehensions, and much more A detailed case study of porting a major library from Python 2 to Python 3 A comprehensive appendix of all the syntactic and semantic changes in Python 3 This is the perfect resource for you if you need to port applications to Python 3, or if you like to jump into languages fast and get going right away.

高性能MySQL by Baron Schwartz / Peter Zaitsev / Vadim Tkachenko / Jeremy D.Zawodny / Arjen Lent / Derek J.Ballin 本书荣获2009年Jolt图书大奖，是不可多得的分享MySQL实用经验的图书。它不但可以帮助MySQL初学者提高使用技巧，更为有经验的MySQL DBA指出了开发高性能MySQL应用的途径。全书包含14章和4个附录，内容覆盖MySQL系统架构、设计应用技巧、SQL语句优化、服务器性能调优、系统配置管理和安全设置、监控分析，以及复制、扩展和备份/还原等主题，每一章的内容自成体系，适合各领域技术人员作选择性的阅读。

计算机安全导论 : 深度实践 by 杜文亮 本书涵盖计算机和网络空间安全的基础知识，包括应用软件安全、Web安全和网络安全。旨在帮助读者了解各种各样的攻击和防御的思想与原理。