A thought-provoking and wide-ranging exploration of machine learning and the race to build computer intelligences as flexible as our own
In the world's top research labs and universities, the race is on to invent the ultimate learning algorithm: one capable of discovering any knowledge from data, and doing anything we want, before we even ask. In The Master Algorithm, Pedro Domingos lifts the veil to give us a peek inside the learning machines that power Google, Amazon, and your smartphone. He assembles a blueprint for the future universal learner--the Master Algorithm--and discusses what it will mean for business, science, and society. If data-ism is today's philosophy, this book is its bible.

《非线性最优化基础》从凸分析的观点全面系统地介绍了非线性最优化的基本理论，是国际著名优化专家Masao Fulkushima教授的最新力作。书中不仅详尽透彻地讲解了(光滑与非光滑优化问题、半定规划问题等)各类优化问题的最优性理论、稳定性理论、灵敏度分析、对偶性理论以及相关的凸分析基础等，还深入介绍了变分不等式问题、非线性互补问题以及均衡约束数学规划问题等均衡问题的最新结果。
《非线性最优化基础》既可作为相关专业高年级本科生和研究生的教材，也可作为相关科研人员的参考书。

面对科技的迅猛发展，中国政府制定了《新一代人工智能发展规划》，将人工智能上升到国家战略层面，并提出：不仅人工智能产业要成为新的经济增长点，而且要在2030年达到世界领先水平，让中国成为世界主要人工智能创新中心，为跻身创新型国家前列和经济强国奠定基础。
《人工智能》一书由腾讯一流团队与工信部高端智库倾力创作。本书从人工智能这一颠覆性技术的前世今生说起，对人工智能产业全貌、最新进展、发展趋势进行了清晰的梳理，对各国的竞争态势做了深入研究。本书还对人工智能给个人、企业、社会带来的机遇与挑战进行了深入分析。对于想全面了解人工智能的读者，本书提供了重要参考，是一本必备书籍。

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Marvin Minsky -- one of the fathers of computer science and cofounder of the Artificial Intelligence Laboratory at MIT -- gives a revolutionary answer to the age-old question: _How does the mind work?_

（马文。明斯基————电脑科学的鼻祖，麻省理工学院的人工智能实验室的创始人之一————在本书里对相传以久的问题，“思维是怎么一回事儿？”，做出了革命性的回答。）

Minsky brilliantly portrays the mind as a _society_ of tiny components that are themselves mindless. Mirroring his theory, Minsky boldly casts The Society of Mind as an intellectual puzzle whose pieces are assembled along the way. Each chapter -- on a self-contained page -- corresponds to a piece in the puzzle. As the pages turn, a unified theory of the mind emerges, like a mosaic. Ingenious, amusing, and easy to read, The Society of Mind is an adventure in imagination.

（明斯基的精彩理论把思维描画成由本身不具备思维的微小部件组成的“社会”。本书章节段落之间结构跟他的理论相呼应，每一页纸独立成为一章，讨论整个问题里的单个环节。翻过这一篇篇书页，关于思维的统一理论渐渐成型，《意识社会》一书妙趣横生，是在想象空间里的一场历险。）

本书是第一本全面阐述如何构建健壮和准确的多语自然语言处理系统的图书，由两位资深专家编辑，集合了该领域众多尖端进展以及从广泛的研究和产业实践中总结出的实用解决方案。第一部分介绍现代自然语言处理的核心概念和理论基础，展示了如何理解单词和文档结构、分析语法、建模语言、识别蕴涵和检测冗余。第二部分彻底阐述与构建真实应用有关的实际考量，包括信息抽取、机器翻译、信息检索、文摘、问答、提炼、处理流水线等。

Thirty-eight chapters, comissioned from experts all over the world, describe major concepts, methods, and applications in computational linguistics. Part I, Linguistic Fundamentals, provides an overview of the field suitable for senior undergraduates and non-specialists from other fields of linguistics and related disciplines. Part II describes current tasks, techniques, and tools in Natural Language Processing and aims to meet the needs of post-doctoral workers and others embarking on computational language research. Part III surveys current applications. This book is a state-of-the-art reference to one of the most active and productive fields in linguistics. It will be of interest and practical use to a wide range of linguists, as well as to researchers in such fields as informatics, artificial intelligence, language engineering, and cognitive science.

《心智、语言和机器:维特根斯坦哲学和人工智能科学的对话》向读者介绍人工智能科学的基本发展历史和基本技术；介绍“人工智能哲学”这门哲学分支的大致发展情况；维氏哲学将对知识表征、自然语言理解、机器人、非单调推理等人工智能的子课题做出贡献。而《心智、语言和机器:维特根斯坦哲学和人工智能科学的对话》就将负责在维氏和这些课题之间搭建桥梁。

《神经信息学(神经系统的理论和模型)》把半个世纪以来有实验依据的神经系统(脑)中的主要理论和模型集中起来，给研究生们提供理论训练。这些理论和模型中有重要基础理论意义的，大都取材于诺贝尔奖获得者的工作（Hodgkin、Huxley、Hartline、Gabor、Bekesy、Eccles、Crick、Edelmen、Sperry……），也包括对信息科学、工程应用有重大影响的假设、理论和算法（Hebb学习律、平行分布式理论框架……）。
《神经信息学(神经系统的理论和模型)》共分三篇：第一篇包括固定结构的神经系统的理论模型，涵盖神经元模型、感受器的数学描述、节律产生和视觉信息加工等；第二篇是关于学习和记忆的理论模型，包括}tebb学习律、平行分布式理论框架、Hopfield模型以及短时程的突触修正规律，清晰讲述了神经系统的理论研究对人工智能、信息科学工程应用的意义；第三篇介绍神经科学和脑科学中当前的几个热点，包括神经编码、功能柱的结构和功能、脑的非线性和意识问题。
《神经信息学(神经系统的理论和模型)》可作为神经科学、认知科学、心理学等专业的研究生教材，也可作为人工视觉、神经假肢、人工智能、信息科学专业研究人员的参考书。

"Written by three experts in the field, Deep Learning is the only comprehensive book on the subject." -- Elon Musk, co-chair of OpenAI; co-founder and CEO of Tesla and SpaceX
Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning.
The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models.
Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.

《支持向量机：理论、算法与拓展》以分类问题（模式识别、判别分析）和回归问题为背景，介绍支持向量机的基本理论、方法和应用。特别强调对所讨论的问题和处理方法的实质进行直观的解释和说明，因此具有很强的可读性。为使具有一般高等数学知识的读者能够顺利阅读，书中首先介绍了最优化的基础知识。《支持向量机：理论、算法与拓展》可作为理工类、管理学等专业的高年级本科生、研究生和教师的教材或教学参考书，也可供相关领域的科研人员和实际工作者阅读参考。

本书是诺贝尔经济学奖得主司马贺的代表作之一，是1996年修订出版的第三版的中译本。第三版增加了全新的一章“复杂性面面观”，修改了文字内容并更新了部分数据。
作者率先构造出“人工科学”的概念，据以将经济学、思维心理学、学习科学、设计科学、管理学、复杂性研究等领域贯穿联系起来，使人获得很多启迪。本书对传统思维提出挑战，指出人工智能所期望达到的领域，揭示了人工性和层级对于复杂性的意义，从多个领域说明人工科学何以是可能的，并试图说明它的性质。本书的基本主题是：物质符号系统具备必要和充分的手段来采取智能行动。

本书是1978年诺贝尔经济学奖得主赫尔伯特・A・西蒙的自传。西蒙是本世纪文理兼通的一位科学奇才。他从研究社科学起步。却在政治科学、经济学、心理学、管理科学、计算机科学和科学哲学等领域颇有建树。从50年代开始，他把关于组织地为决策管理方面的理论应用于经济学，还开创了人工智能，认知科学和信处理心理学等新学科，开辟了探索人类思维和智能的新天地。全书分四部分，西蒙用一个跨学科专家的眼光扫描了20世纪下半叶众多学科领域的发展以及世界政治、经济局势的变化。其间他还以时间和地域为经纬，穿插点缀着自己对生活、爱情、友谊及科研等往事的回忆。从内向聪慧的孩提时期的种种轶事，到成年后获诺贝尔奖时的庄严时刻，作者个人的历史伴随着他幽默智慧的语言，活跃机敏的思想渐次闪现了出来，与时代的背景融合在一起，绘成了一幅五色斑谰的人生画卷。读完本书，任何喜欢思考的人都不难发现，现代科技与社会科学相结合后会对人类社会产生多么深远的影响。

The dramatic growth in practical applications for machine learning over the last ten years has been accompanied by many important developments in the underlying algorithms and techniques. For example, Bayesian methods have grown from a specialist niche to become mainstream, while graphical models have emerged as a general framework for describing and applying probabilistic techniques. The practical applicability of Bayesian methods has been greatly enhanced by the development of a range of approximate inference algorithms such as variational Bayes and expectation propagation, while new models based on kernels have had a significant impact on both algorithms and applications.
This completely new textbook reflects these recent developments while providing a comprehensive introduction to the fields of pattern recognition and machine learning. It is aimed at advanced undergraduates or first-year PhD students, as well as researchers and practitioners. 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.
The book is suitable for courses on machine learning, statistics, computer science, signal processing, computer vision, data mining, and bioinformatics. Extensive support is provided for course instructors, including more than 400 exercises, graded according to difficulty. Example solutions for a subset of the exercises are available from the book web site, while solutions for the remainder can be obtained by instructors from the publisher. The book is supported by a great deal of additional material, and the reader is encouraged to visit the book web site for the latest information.

《人工智能:一种现代方法》(第2版中文版)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，是一本难得的综合性教材。全书分为八大部分：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“通讯、感知与行动” ，第八部分“ 结论” 。

Statistical approaches to processing natural language text have become dominant in recent years. This foundational text is the first comprehensive introduction to statistical natural language processing (NLP) to appear. The book contains all the theory and algorithms needed for building NLP tools. It provides broad but rigorous coverage of mathematical and linguistic foundations, as well as detailed discussion of statistical methods, allowing students and researchers to construct their own implementations. The book covers collocation finding, word sense disambiguation, probabilistic parsing, information retrieval, and other applications.

《群智能:介绍与应用》是一部关于群智能方法及其应用的专业书籍。第一部分导论介绍群智能、优化和群机器人的生物学基础，以及在新一代通信网络中的应用。优化和群机器人学是现今群智能原理应用非常成功的两个领域。第二部分的章节包含群智能研究的一些具体实例，例如，机器人行为的演变、粒子群动态优化、有机计算和生产网络中的非集中式流量。书中的八章相对独立，读者可以根据自己的研究领域和兴趣选择其中相应的章节阅读。《群智能:介绍与应用》适合以人工智能、通信网络或生物信息学为研究方向的理工科大学研究生和教师阅读，也可供相关研究领域的科研人员参考。

《模式分类》(原书第2版)的第1版《模式分类与场景分析》出版于1973年，是模式识别和场景分析领域奠基性的经曲名著。在第2版中，除了保留了第1版的关于统计模式识别和结构模式识别的主要内容以外，读者将会发现新增了许多近25年来的新理论和新方法，其中包括神经网络、机器学习、数据挖掘、进化计算、不变量理论、隐马尔可夫模型、统计学习理论和支持向量机等。作者还为未来25年的模式识别的发展指明了方向。书中包含许多实例，各种不同方法的对比，丰富的图表，以及大量的课后习题和计算机练习。

本书介绍了统计学习理论和支持向量机的关键思想、结论和方法，以及该领域的最新进展。统计学习理论是针对小样本情况研究统计学习规律的理论，是传统统计学的重要发展和补充。其核心思想是通过控制学习机器的容量实现对推广能力的控制。由Springer-Verlag出版社授权出版。

《控制论(或关于在动物和机器中控制和通信的科学)第2版》是由科学出版社出版的。