美国计算机科学与工程教授，AI应用于游戏的发起人

机器挑战人类的时代，探索学习、认知和思维的新模式

◎ 编辑推荐

☆ 美国计算机科学与工程教授，AI应用于游戏的发起人

☆ 介绍了*前沿的AI技术及其应用

☆ 以AI的视野，进入游戏世界，探索智能时代学习、认知和思维的新模式

◎ 内容简介

机器不断挑战人类的认知能力，而游戏设计的核心 就是认知科学，设计游戏需要了解人类如何思维和学习。游戏从来都不只是一种自我消遣娱乐的方式，它也是学习和思考的工具。

那么，当机器在所有游戏中击败人类，是不是意味着人工智能已经超越人类？我们又能从人工智能的不断进化中学到什么？

本书全方位介绍了人工智能的最新进展，同时列举了《超级马里奥兄弟》《愤怒的小鸟》《DOTA》《塞尔达传说》等数十款游戏史上不同类型的爆款游戏，简明易懂地阐明了游戏、学习与人工智能的关系。我们会看到，通过学习怎样玩游戏、怎样设计游戏、怎样将游戏用于人工智能开发，可以更好地了解人类和机器是怎样思考的，并进一步探索游戏、学习和思维的新模式。

统计学习方法即机器学习方法，是计算机及其应用领域的一门重要学科。本书分为监督学 习和无监督学习两篇，全面系统地介绍了统计学习的主要方法。包括感知机、k 近邻法、朴素贝叶斯法、决策树、逻辑斯谛回归与最大熵模型、支持向量机、提升方法、EM 算法、隐马尔可夫模型和条件随机场，以及聚类方法、奇异值分解、主成分分析、潜在语义分析、概率潜在语义分析、马尔可夫链蒙特卡罗法、潜在狄利克雷分配和 PageRank 算法等。除有关统计学习、监督学习和无监督学习的概论和总结的四章外，每章介绍一种方法。叙述力求从具体问题或实例入手， 由浅入深，阐明思路，给出必要的数学推导，便于读者掌握统计学习方法的实质，学会运用。 为满足读者进一步学习的需要，书中还介绍了一些相关研究，给出了少量习题，列出了主要参考文献。 本书是统计机器学习及相关课程的教学参考书，适用于高等院校文本数据挖掘、信息检索及自然语言处理等专业的大学生、研究生，也可供从事计算机应用相关专业的研发人员参考。

这是一部从技术原理、算法和工程实践3个维度系统讲解图像识别的著作，由阿里巴巴达摩院算法专家、阿里巴巴技术发展专家、阿里巴巴数据架构师联合撰写。

在知识点的选择上，本书广度和深度兼顾，既能让完全没有基础的读者迅速入门，又能让有基础的读者深入掌握图像识别的核心技术；在写作方式上，本书避开了复杂的数学公式及其推导，从问题的前因后果 、创造者的思考过程，利用简单的数学计算来做模型分析和讲解，通俗易懂。更重要的是，本书不仅仅是聚焦于技术，而是将重点放在了如何用技术解决实际的业务问题。

全书一共13章：

第1-2章主要介绍了图像识别的应用场景、工具和工作环境的搭建；

第3-6章详细讲解了图像分类算法、机器学习、神经网络、误差反向传播等图像识别的基础技术及其原理；

第7章讲解了如何利用PyTorch来实现神经网络的图像分类，专注于实操，是从基础向高阶的过渡；

第8-12章深入讲解了图像识别的核心技术及其原理，包括卷积神经网络、目标检测、分割、产生式模型、神经网络可视化等主题；

第13章从工程实践的角度讲解了图像识别算法的部署模式。

购买本书的读者请在http://www.hzcourse.com/web/refbook/detail/8376/226

下载源代码

本书全面阐述了模式识别的基础理论、最新方法以及各种应用。模式识别是信息科学和人工智能的重要组成部分，主要应用领域有图像分析、光学字符识别、信道均衡、语言识别和音频分类等。本书在完美地结合当前的理论与实践的基础上，讨论了贝叶斯分类、贝叶斯网络、线性和非线性分类器设计、上下文相关分类、特征生成、特征选取技术、学习理论的基本概念以及聚类概念与算法。与前一版相比，增加了大数据集和高维数据相关的最新算法，这些算法适用于Web挖掘和生物信息等应用；提供了最新的分类器和鲁棒回归的核方法；分类器组合技术，包括Boosting方法。新增一些热点问题，如非线性降维、非负矩阵因数分解、关联性反馈、鲁棒回归、半监督学习、谱聚类和聚类组合技术。每章均提供有习题与练习，用MATLAB求解问题，给出一些例题的多种求解方法；且支持网站上提供有习题解答，以便于读者增加实际经验。 本书可作为高等院校自动化、计算机、电子和通信等专业研究生和高年级本科生的教材，也可作为计算机信息处理、自动控制等相关领域的工程技术人员的参考用书。

An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform. Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.

大数据是当前最为流行的热点概念之一，其已由技术名词衍生到对很多行业产生颠覆性影响的社会现象，作为最明确的技术发展趋势之一，基于大数据的各种新型产品必将会对每个人的日常生活产生日益重要的影响。 《大数据日知录：架构与算法》从架构与算法角度全面梳理了大数据存储与处理的相关技术。大数据技术具有涉及的知识点异常众多且正处于快速演进发展过程中等特点，其技术点包括底层的硬件体系结构、相关的基础理论、大规模数据存储系统、分布式架构设计、各种不同应用场景下的差异化系统设计思路、机器学习与数据挖掘并行算法以及层出不穷的新架构、新系统等。《大数据日知录：架构与算法》对众多纷繁芜杂的相关技术文献和系统进行了择优汰劣并系统性地对相关知识分门别类地进行整理和介绍，将大数据相关技术分为大数据基础理论、大数据系统体系结构、大数据存储，以及包含批处理、流式计算、交互式数据分析、图数据库、并行机器学习的架构与算法以及增量计算等技术分支在内的大数据处理等几个大的方向。通过这种体系化的知识梳理与讲解，相信对于读者整体和系统地了解、吸收和掌握相关的优秀技术有极大的帮助与促进作用。 《大数据日知录：架构与算法》的读者对象包括对NoSQL 系统及大数据处理感兴趣的所有技术人员，以及有志于投身到大数据处理方向从事架构师、算法工程师、数据科学家等相关职业的在校本科生及研究生。

本书全面介绍了数据挖掘，涵盖了五个主题：数据、分类、关联分析、聚类和异常检测。除异常检测外，每个主题都有两章。前一章涵盖基本概念、代表性算法和评估技术，而后一章讨论高级概念和算法。这样读者在透彻地理解数据挖掘的基础的同时，还能够了解更多重要的高级主题。 本书是明尼苏达大学和密歇根州立大学数据挖掘课程的教材，由于独具特色，正式出版之前就已经被斯坦福大学、得克萨斯大学奥斯汀分校等众多名校采用。 本书特色  与许多其他同类图书不同，本书将重点放在如何用数据挖掘知识解决各种实际问题。  只要求具备很少的预备知识——不需要数据库背景，只需要很少的统计学或数学背景知识。  书中包含大量的图表、综合示例和丰富的习题，并且使用示例、关键算法的简洁描述和习题，尽可能直接地聚焦于数据挖掘的主要概念。  教辅内容极为丰富，包括课程幻灯片、学生课题建议、数据挖掘资源（如数据挖掘算法和数据集）、联机指南（使用实际的数据集和数据分析软件，为本书介绍的部分数据挖掘技术提供例子讲解）。  向采用本书作为教材的教师提供习题解答。

With so much more music available these days, traditional ways of finding music have diminished. Today radio shows are often programmed by large corporations that create playlists drawn from a limited pool of tracks. Similarly, record stores have been replaced by big-box retailers that have ever-shrinking music departments. Instead of relying on DJs, record-store clerks or their friends for music recommendations, listeners are turning to machines to guide them to new music. In this book, Òscar Celma guides us through the world of automatic music recommendation. He describes how music recommenders work, explores some of the limitations seen in current recommenders, offers techniques for evaluating the effectiveness of music recommendations and demonstrates how to build effective recommenders by offering two real-world recommender examples. He emphasizes the user's perceived quality, rather than the system's predictive accuracy when providing recommendations, thus allowing users to discover new music by exploiting the long tail of popularity and promoting novel and relevant material ("non-obvious recommendations"). In order to reach out into the long tail, he needs to weave techniques from complex network analysis and music information retrieval. Aimed at final-year-undergraduate and graduate students working on recommender systems or music information retrieval, this book presents the state of the art of all the different techniques used to recommend items, focusing on the music domain as the underlying application.

The first edition, published in 1973, has become a classic reference in the field. Now with the second edition, readers will find information on key new topics such as neural networks and statistical pattern recognition, the theory of machine learning, and the theory of invariances. Also included are worked examples, comparisons between different methods, extensive graphics, expanded exercises and computer project topics. An Instructor's Manual presenting detailed solutions to all the problems in the book is available from the Wiley editorial department.

This important new text and reference for researchers and students in machine learning, game theory, statistics and information theory offers the first comprehensive treatment of the problem of predicting individual sequences. Unlike standard statistical approaches to forecasting, prediction of individual sequences does not impose any probabilistic assumption on the data-generating mechanism. Yet, prediction algorithms can be constructed that work well for all possible sequences, in the sense that their performance is always nearly as good as the best forecasting strategy in a given reference class. The central theme is the model of prediction using expert advice, a general framework within which many related problems can be cast and discussed. Repeated game playing, adaptive data compression, sequential investment in the stock market, sequential pattern analysis, and several other problems are viewed as instances of the experts' framework and analyzed from a common nonstochastic standpoint that often reveals new and intriguing connections. Old and new forecasting methods are described in a mathematically precise way in order to characterize their theoretical limitations and possibilities.

北京机器学习是计算机科学和人工智能中非常重要的一个研究领域，近年来，机器学习不但在计算机科学的众多领域中大显身手，而且成为一些交叉学科的重要支撑技术。本书邀请国内外相关领域的专家撰文，以综述的形式介绍机器学习中不同领域的研究进展。全书共分13章。第1章是关于机器学习的一个全局性综述。第2至第6章分别对统计学习、非监督学习、符号学习、强化学习和流形学习进行了综述，并穿插了作者的一些精彩工作。第7和第8章分别介绍了作者在集成学习和进化学习中某一具体话题上的研究成果。第9和第10章对数据挖掘中的一些问题进行了介绍和讨论。第11至第13章则对机器学习在模式识别、视频信息处理等领域的应用做了介绍。 本书可供计算机、自动化及相关专业的学生、教师、研究生和工程技术人员参考。

Authors introduce a number of central topics in computational learning theory for researchers and students in artificial intelligence, neural networks, theoretical computer science, and statistics. DLC: Machine learning.

Introduction to Data Mining presents fundamental concepts and algorithms for those learning data mining for the first time. Each concept is explored thoroughly and supported with numerous examples. The text requires only a modest background in mathematics. Each major topic is organized into two chapters, beginning with basic concepts that provide necessary background for understanding each data mining technique, followed by more advanced concepts and algorithms. Quotes This book provides a comprehensive coverage of important data mining techniques. Numerous examples are provided to lucidly illustrate the key concepts. -Sanjay Ranka, University of Florida In my opinion this is currently the best data mining text book on the market. I like the comprehensive coverage which spans all major data mining techniques including classification, clustering, and pattern mining (association rules). -Mohammed Zaki, Rensselaer Polytechnic Institute

As with any burgeoning technology that enjoys commercial attention, the use of data mining is surrounded by a great deal of hype. Exaggerated reports tell of secrets that can be uncovered by setting algorithms loose on oceans of data. But there is no magic in machine learning, no hidden power, no alchemy. Instead there is an identifiable body of practical techniques that can extract useful information from raw data. This book describes these techniques and shows how they work. The book is a major revision of the first edition that appeared in 1999. While the basic core remains the same, it has been updated to reflect the changes that have taken place over five years, and now has nearly double the references. The highlights for the new edition include thirty new technique sections; an enhanced Weka machine learning workbench, which now features an interactive interface; comprehensive information on neural networks; a new section on Bayesian networks; plus much more; algorithmic methods at the heart of successful data mining-including tried and true techniques as well as leading edge methods; performance improvement techniques that work by transforming the input or output; and, downloadable Weka, a collection of machine learning algorithms for data mining tasks, including tools for data pre-processing, classification, regression, clustering, association rules, and visualization-in a new, interactive interface.

在刚过去的十年我们见证了共享Web和社会媒体的诞生，它们用各种富有创意的方式将人们联系在一起。目前，成千上万的用户忙着在线玩、加标签、工作以及开展社交活动，合作、通信和智能正采取着前所未有的新形式。社会媒体的出现促进了商业模式的改变，影响了人们观点和情感的沟通，为大规模地研究人际交互和集体行为提供了无数机会。 本书从数据挖掘角度介绍社会媒体的性质，评述社会媒体计算的代表性成果，并描述社会媒体带来的挑战。书中介绍了基本概念，使用浅显易懂的例子展示最新的和有效的评价方法。特别是讨论了基于图的社区发现技术并对处理社会媒体中动态的、混杂的网络进行了重要延伸。另外还展示了发现的社区模式怎样用于社会媒体挖掘。本书中的概念、算法和方法能够帮助人们更好地利用社会媒体，并为建立社会化智能系统提供支持。本书是研究社会媒体中社区发现与挖掘技术的入门级读物，适合以数据为中心的社会媒体学科的学生、研究者和实践者阅读。 本书网站http://dmml.asu.edu/cdm/提供了讲课幻灯片、书中所有的图、主要的参考文献、书中使用的一些小型数据集，以及一些代表性算法的源代码。

Now that storage and collection technologies are cheaper and more precise, methods for extracting relevant information from large datasets is within the reach any experienced programmer willing to crunch data. With this book, you'll learn machine learning and statistics tools in a practical fashion, using black-box solutions and case studies instead of a traditional math-heavy presentation. By exploring each problem in this book in depth - including both viable and hopeless approaches - you'll learn to recognize when your situation closely matches traditional problems. Then you'll discover how to apply classical statistics tools to your problem. Machine Learning for Hackers is ideal for programmers from private, public, and academic sectors.

The popularity of the Web and Internet commerce provides many extremely large datasets from which information can be gleaned by data mining. This book focuses on practical algorithms that have been used to solve key problems in data mining and which can be used on even the largest datasets. It begins with a discussion of the map-reduce framework, an important tool for parallelizing algorithms automatically. The authors explain the tricks of locality-sensitive hashing and stream processing algorithms for mining data that arrives too fast for exhaustive processing. The PageRank idea and related tricks for organizing the Web are covered next. Other chapters cover the problems of finding frequent itemsets and clustering. The final chapters cover two applications: recommendation systems and Web advertising, each vital in e-commerce. Written by two authorities in database and Web technologies, this book is essential reading for students and practitioners alike.

This book presents an integrated collection of representative approaches for scaling up machine learning and data mining methods on parallel and distributed computing platforms. Demand for parallelizing learning algorithms is highly task-specific: in some settings it is driven by the enormous dataset sizes, in others by model complexity or by real-time performance requirements. Making task-appropriate algorithm and platform choices for large-scale machine learning requires understanding the benefits, trade-offs and constraints of the available options. Solutions presented in the book cover a range of parallelization platforms from FPGAs and GPUs to multi-core systems and commodity clusters, concurrent programming frameworks including CUDA, MPI, MapReduce and DryadLINQ, and learning settings (supervised, unsupervised, semi-supervised and online learning). Extensive coverage of parallelization of boosted trees, SVMs, spectral clustering, belief propagation and other popular learning algorithms and deep dives into several applications make the book equally useful for researchers, students and practitioners.

We live in a new age for statistical inference, where modern scientific technology such as microarrays and fMRI machines routinely produce thousands and sometimes millions of parallel data sets, each with its own estimation or testing problem. Doing thousands of problems at once is more than repeated application of classical methods. Taking an empirical Bayes approach, Bradley Efron, inventor of the bootstrap, shows how information accrues across problems in a way that combines Bayesian and frequentist ideas. Estimation, testing and prediction blend in this framework, producing opportunities for new methodologies of increased power. New difficulties also arise, easily leading to flawed inferences. This book takes a careful look at both the promise and pitfalls of large-scale statistical inference, with particular attention to false discovery rates, the most successful of the new statistical techniques. Emphasis is on the inferential ideas underlying technical developments, illustrated using a large number of real examples.

本书涵盖了五类重要的智能算法：搜索、推荐、聚类、分类和分类器组合，并结合具体的案例讨论了它们在Web应用中的角色及要注意的问题。除了第1章的概要性介绍以及第7章对所有技术的整合应用外，第2～6章以代码示例的形式分别对这五类算法进行了介绍。 本书面向的是广大普通读者，特别是对算法感兴趣的工程师与学生，所以对于读者的知识背景并没有过多的要求。本书中的例子和思想应用广泛，所以对于希望从业务角度更好地理解有关技术的技术经理、产品经理和管理层来说，本书也有一定的价值。