Learning Data Science

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1.

Artificial Communication

出版日：2022/04/05 作者：Elena Esposito 出版社：Mit Pr 裝訂：精裝

A proposal that we think about digital technologies such as machine learning not in terms of artificial intelligence but as artificial communication. Algorithms that work with deep learning and big data are getting so much better at doing so many things that it makes us uncomfortable. How can a device know what our favorite songs are, or what we should write in an email? Have machines become too smart? In Artificial Communication, Elena Esposito argues that drawing this sort of analogy between algorithms and human intelligence is misleading. If machines contribute to social intelligence, it will not be because they have learned how to think like us but because we have learned how to communicate with them. Esposito proposes that we think of “smart” machines not in terms of artificial intelligence but as artificial communication. To do this, we need a concept of communication that can take into account the possibility that a communication partner may not be a human being but an algor

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Reality Mining ─ Using Big Data to Engineer a Better World

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2.

Reality Mining ─ Using Big Data to Engineer a Better World

出版日：2016/10/07 作者：Nathan Eagle; Kate Greene 出版社：Mit Pr 裝訂：平裝

Big Data is made up of lots of little data: numbers entered into cell phones, addresses entered into GPS devices, visits to websites, online purchases, ATM transactions,

優惠:外文書周末優惠-單79雙75 優惠價： 79 601

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3.

Data Science

出版日：2018/04/13 作者：John D. Kelleher; Brendan Tierney 出版社：Mit Pr 裝訂：平裝

A concise introduction to the emerging field of data science, explaining its evolution, relation to machine learning, current uses, data infrastructure issues, and ethical challenges.The goal of data

優惠:外文書周末優惠-單79雙75 優惠價： 79 509

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4.

Outer Limits of Reason

出版日：2016/11/04 作者：Noson S. Yanofsky 出版社：Mit Pr 裝訂：平裝

Many books explain what is known about the universe. This book investigates what cannot be known. Rather than exploring the amazing facts that science, mathematics, and r

優惠:外文書周末優惠-單79雙75 優惠價： 79 599

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5.

Human-Centered Data Science

出版日：2022/02/01 作者：Cecilia Aragon 出版社：Mit Pr 裝訂：平裝

Best practices for addressing the bias and inequality that may result from the automated collection, analysis, and distribution of large datasets.Human-centered data science is a new interdisciplinary field that draws from human-computer interaction, social science, statistics, and computational techniques. This book, written by founders of the field, introduces best practices for addressing the bias and inequality that may result from the automated collection, analysis, and distribution of very large datasets. It offers a brief and accessible overview of many common statistical and algorithmic data science techniques, explains human-centered approaches to data science problems, and presents practical guidelines and real-world case studies to help readers apply these methods. The authors explain how data scientists’ choices are involved at every stage of the data science workflow―and show how a human-centered approach can enhance each one, by making the process more transparent, aski

優惠:外文書周末優惠-單79雙75 優惠價： 79 1051

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Elements of Causal Inference ─ Foundations and Learning Algorithms

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6.

Elements of Causal Inference ─ Foundations and Learning Algorithms

出版日：2017/11/29 作者：Jonas Peters; Dominik Janzing; Bernhard Sch?女opf 出版社：Mit Pr 裝訂：精裝

The mathematization of causality is a relatively recent development, and has become increasingly important in data science and machine learning. This book offers a self-contained and concise introduct

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7.

Machine Learning from Weak Supervision

出版日：2022/08/23 作者：Masashi Sugiyama 出版社：Mit Pr 裝訂：精裝

Fundamental theory and practical algorithms of weakly supervised classification, emphasizing an approach based on empirical risk minimization.Standard machine learning techniques require large amounts of labeled data to work well. When we apply machine learning to problems in the physical world, however, it is extremely difficult to collect such quantities of labeled data. This book presents theory and algorithms for weakly supervised learning, a paradigm of machine learning from weakly labeled data. Emphasizing an approach based on empirical risk minimization and drawing on state-of-the-art research in weakly supervised learning, the book provides both the fundamentals of the field and the advanced mathematical theories underlying them. It can be used as a reference for practitioners and researchers and in the classroom.The book first mathematically formulates classification problems, defines common notations, and reviews various algorithms for supervised binary and multiclass classif

優惠:外文書周末優惠-單79雙75 優惠價： 79 1952

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Machine Learning, revised and updated edition

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8.

Machine Learning, revised and updated edition

出版日：2021/08/17 作者：Ethem Alpaydin 出版社：Mit Pr 裝訂：平裝

A concise overview of machine learning--computer programs that learn from data--the basis of such applications as voice recognition and driverless cars.Today, machine learning underlies a range of app

優惠:外文書周末優惠-單79雙75 優惠價： 79 479

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9.

Machine Learners ― Archaeology of a Data Practice

出版日：2017/12/08 作者：Adrian Mackenzie 出版社：Mit Pr 裝訂：平裝

If machine learning transforms the nature of knowledge, does it also transform the practice of critical thought?Machine learning-programming computers to learn from data-has spread across scientific d

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Fundamentals of Machine Learning for Predictive Data Analytics ─ Algorithms, Worked Examples, and Case Studies

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10.

Fundamentals of Machine Learning for Predictive Data Analytics ─ Algorithms, Worked Examples, and Case Studies

出版日：2015/07/24 作者：John D. Kelleher; Brian MAC Namee; Aoife D'arcy 出版社：Mit Pr 裝訂：精裝

Machine learning is often used to build predictive models by extracting patterns from large datasets. These models are used in predictive data analytics applications including price prediction, risk a

優惠:外文書周末優惠-單79雙75 優惠價： 79 2402

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11.

Introduction to Machine Learning

出版日：2014/08/22 作者：Ethem Alpaydin 出版社：Mit Pr 裝訂：精裝

The goal of machine learning is to program computers to use example data or pastexperience to solve a given problem. Many successful applications of machine learning exist already,including systems th

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Machine Learning ─ A Probabilistic Perspective

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12.

Machine Learning ─ A Probabilistic Perspective

出版日：2012/08/24 作者：Kevin P. Murphy 出版社：Mit Pr 裝訂：精裝

Today's Web-enabled deluge of electronic data calls for automated methods of dataanalysis. Machine learning provides these, developing methods that can automatically detect patternsin data and then us

優惠:外文書周末優惠-單79雙75 優惠價： 79 5214

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13.

Introduction to Machine Learning

出版日：2009/12/04 作者：Ethem Alpaydin 出版社：Mit Pr 裝訂：精裝

The goal of machine learning is to program computers to use example data or past experience to solve a given problem. Many successful applications of machine learning exist already, including systems

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14.

Introduction To Machine Learning

出版日：2004/10/15 作者：Ethem Alpaydin 出版社：Mit Pr 裝訂：精裝

The goal of machine learning is to program computers to use example data or past experience to solve a given problem. Many successful applications of machine learning exist already, including systems

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15.

High-Performance Big Data Computing

出版日：2022/06/21 作者：Dhabaleswar K. Panda 出版社：Mit Pr 裝訂：精裝

An in-depth overview of an emerging field that brings together high-performance computing, big data processing, and deep lLearning. Over the last decade, the exponential explosion of data known as big data has changed the way we understand and harness the power of data. The emerging field of high-performance big data computing, which brings together high-performance computing (HPC), big data processing, and deep learning, aims to meet the challenges posed by large-scale data processing. This book offers an in-depth overview of high-performance big data computing and the associated technical issues, approaches, and solutions. The book covers basic concepts and necessary background knowledge, including data processing frameworks, storage systems, and hardware capabilities; offers a detailed discussion of technical issues in accelerating big data computing in terms of computation, communication, memory and storage, codesign, workload characterization and benchmarking, and system deplo

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16.

Machine Learners ─ Archaeology of a Data Practice

出版日：2017/11/16 作者：Adrian Mackenzie 出版社：Mit Pr 裝訂：精裝

Machine learning -- programming computers to learn from data -- has spread across scientific disciplines, media, entertainment, and government. Medical research, autonomous vehicles, credit transactio

優惠:外文書周末優惠-單79雙75 優惠價： 79 1201

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Big Data, Little Data, No Data ─ Scholarship in the Networked World

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17.

Big Data, Little Data, No Data ─ Scholarship in the Networked World

出版日：2017/02/03 作者：Christine L. Borgman 出版社：Mit Pr 裝訂：平裝

"Big Data" is on the covers of Science, Nature, the Economist, and Wired magazines, on the front pages of the Wall Street Journal and the New York Times. B

優惠:外文書周末優惠-單79雙75 優惠價： 79 901

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18.

Big Data, Little Data, No Data ─ Scholarship in the Networked World

出版日：2015/01/02 作者：Christine L. Borgman 出版社：Mit Pr 裝訂：精裝

"Big Data" is on the covers of Science, Nature, the Economist, andWired magazines, on the front pages of the Wall Street Journal and theNew York Times. But despite the media hyperbol

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19.

Optimization for Machine Learning

出版日：2011/09/30 作者：Suvrit Sra; Sebastian Nowozin; Stephen J. Wright 出版社：Mit Pr 裝訂：精裝

The interplay between optimization and machine learning is one of the most importantdevelopments in modern computational science. Optimization formulations and methods are proving tobe vital in design

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Variability and Consistency in Early Language Learning：The Wordbank Project

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20.

Variability and Consistency in Early Language Learning：The Wordbank Project

出版日：2021/03/16 作者：Michael C. Frank; Mika Braginsky 出版社：Mit Pr 裝訂：精裝

A data-driven exploration of how children’s language learning varies across different languages, providing both a theoretical framework and reference.The Wordbank Project examines variability and cons

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21.

Fndls Of Machine Lrng Fo

出版日：2020/10/20 作者：John D. Kelleher 出版社：Mit Pr 裝訂：精裝

The second edition of a comprehensive introduction to machine learning approaches used in predictive data analytics, covering both theory and practice.Machine learning is often used to build predictiv

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22.

Cultural Analytics

出版日：2020/10/20 作者：Lev Manovich 出版社：Mit Pr 裝訂：精裝

A book at the intersection of data science and media studies, presenting concepts and methods for computational analysis of cultural data. How can we see a billion images? What analytical methods can

優惠:外文書周末優惠-單79雙75 優惠價： 79 1201

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23.

Building Ontologies With Basic Formal Ontology

出版日：2015/07/31 作者：Robert Arp; Barry Smith; Andrew D. Spear 出版社：Mit Pr 裝訂：平裝

In the era of "big data," science is increasingly information driven, and the potential for computers to store, manage, and integrate massive amounts of data has given rise to such n

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24.

Computational Finance 1999

出版日：2000/04/24 作者：YaserS. Abu-Mostafa 出版社：Mit Pr 裝訂：平裝

This book covers the techniques of data mining, knowledge discovery, genetic algorithms, neural networks, bootstrapping, machine learning, and Monte Carlo simulation. Computational finance, an excit

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25.

Artful Science ─ Enlightenment Entertainment and the Eclipse of Visual Education

出版日：1996/02/28 作者：Barbara Maria Stafford 出版社：Mit Pr 裝訂：平裝

What has happened to the magic of learning? Playful illusions, spellbinding games, and lifelike automata were once integral to education. Artful Science reveals the exhilarating but paradoxical inter

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26.

Data Action

出版日：2022/08/09 作者：Sarah Williams 出版社：Mit Pr 裝訂：平裝

How to use data as a tool for empowerment rather than oppression.Big data can be used for good, from tracking disease to exposing human rights violations, and for bad, implementing surveillance and control. Data inevitably represents the ideologies of those who control its use; data analytics and algorithms too often exclude women, the poor, and ethnic groups. In Data Action, Sarah Williams provides a guide for working with data in more ethical and responsible ways. Williams outlines a method that emphasizes collaboration among data scientists, policy experts, data designers, and the public. The approach generates policy debates, influences civic decisions, and informs design to help ensure that the voices of people represented in the data are neither marginalized nor left unheard.

優惠:外文書周末優惠-單79雙75 優惠價： 79 900

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An Experiential Introduction to Principles of Programming Languages

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27.

An Experiential Introduction to Principles of Programming Languages

出版日：2022/05/24 作者：Hridesh Rajan 出版社：Mit Pr 裝訂：精裝

A textbook that uses a hands-on approach to teach principles of programming languages, with Java as the implementation language.This introductory textbook teaches the principles of programming languages by using an experiential learning style, in which students learn about language features by realizing those features in a pedagogical compiler. Students use Java―the most commonly used programming language in the first two years of the computer science curriculum―as the implementation language throughout. The book also discusses a range of emerging topics in programming languages missing from existing textbooks, including concurrency, Big Data, and event-driven programming. The goal is to prepare students to design, implement, analyze, and understand both domain-specific and general-purpose programming languages. The book first develops basic concepts in languages, including means of computation using primitive values, means of combination such as variable definition and functions, an

優惠:外文書周末優惠-單79雙75 優惠價： 79 1802

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All Data Are Local: Thinking Critically in a Data-Driven Society

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28.

All Data Are Local: Thinking Critically in a Data-Driven Society

出版日：2022/05/03 作者：Yanni Alexander Loukissas 出版社：Mit Pr 裝訂：平裝

How to analyze data settings rather than data sets, acknowledging the meaning-making power of the local. In our data-driven society, it is too easy to assume the transparency of data. Instead, Yanni Loukissas argues in All Data Are Local, we should approach data sets with an awareness that data are created by humans and their dutiful machines, at a time, in a place, with the instruments at hand, for audiences that are conditioned to receive them. The term data set implies something discrete, complete, and portable, but it is none of those things. Examining a series of data sources important for understanding the state of public life in the United States--Harvard's Arnold Arboretum, the Digital Public Library of America, UCLA's Television News Archive, and the real estate marketplace Zillow--Loukissas shows us how to analyze data settings rather than data sets.Loukissas sets out six principles: all data are local; data have complex attachments to place; data are collected from heterogen

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29.

Persuading with Data

出版日：2022/03/15 作者：Miro Kazakoff 出版社：Mit Pr 裝訂：平裝

An integrated introduction to data visualization, strategic communication, and delivery best practices.Persuading with Data provides an integrated instructional guide to data visualization, strategic communication, and delivery best practices. Most books on data visualization focus on creating good graphs. This is the first book that combines both explanatory visualization and communication strategy, showing how to use visuals to create effective communications that convince an audience to accept and act on the data. In four parts that proceed from macro to micro, the book explains how our brains make sense of graphs; how to design effective graphs and slides that support your ideas; how to organize those ideas into a compelling presentation; and how to deliver and defend data to an audience. Persuading with Data is for anyone who has to explain analytical results to others. It synthesizes a wide range of skills needed by modern data professionals, providing a complete toolkit for cr

優惠:外文書周末優惠-單79雙75 優惠價： 79 1896

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30.

Probabilistic Machine Learning

出版日：2022/02/01 作者：Kevin P. Murphy 出版社：Mit Pr 裝訂：精裝

A detailed and up-to-date introduction to machine learning, presented through the unifying lens of probabilistic modeling and Bayesian decision theory.This book offers a detailed and up-to-date introduction to machine learning (including deep learning) through the unifying lens of probabilistic modeling and Bayesian decision theory. The book covers mathematical background (including linear algebra and optimization), basic supervised learning (including linear and logistic regression and deep neural networks), as well as more advanced topics (including transfer learning and unsupervised learning). End-of-chapter exercises allow students to apply what they have learned, and an appendix covers notation. Probabilistic Machine Learning grew out of the author’s 2012 book, Machine Learning: A Probabilistic Perspective. More than just a simple update, this is a completely new book that reflects the dramatic developments in the field since 2012, most notably deep learning. In addition, the ne

優惠:外文書周末優惠-單79雙75 優惠價： 79 5925

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Introduction to Modeling Cognitive Processes

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31.

Introduction to Modeling Cognitive Processes

出版日：2022/02/01 作者：Tom Verguts 出版社：Mit Pr 裝訂：精裝

An introduction to computational modeling for cognitive neuroscientists, covering both foundational work and recent developments. Cognitive neuroscientists need sophisticated conceptual tools to make sense of their field’s proliferation of novel theories, methods, and data. Computational modeling is such a tool, enabling researchers to turn theories into precise formulations. This book offers a mathematically gentle and theoretically unified introduction to modeling cognitive processes. Theoretical exercises of varying degrees of difficulty throughout help readers develop their modeling skills. After a general introduction to cognitive modeling and optimization, the book covers models of decision making; supervised learning algorithms, including Hebbian learning, delta rule, and backpropagation; the statistical model analysis methods of model parameter estimation and model evaluation; the three recent cognitive modeling approaches of reinforcement learning, unsupervised learning, and

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32.

Child Data Citizen

出版日：2020/12/22 作者：Veronica Barassi 出版社：Mit Pr 裝訂：平裝

An examination of the datafication of family life--in particular, the construction of our children into data subjects. Our families are being turned into data, as the digital traces we leave are share

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33.

Data Action

出版日：2020/10/27 作者：Sarah Williams 出版社：Mit Pr 裝訂：精裝

How to use data as a tool for empowerment rather than oppression. Big data can be used for good--from tracking disease to exposing human rights violations--and for bad--implementing surveillance and c

優惠:外文書周末優惠-單79雙75 優惠價： 79 1200

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Trusted Data ― A New Framework for Identity and Data Sharing

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34.

Trusted Data ― A New Framework for Identity and Data Sharing

出版日：2019/11/12 作者：Thomas Hardjono ; David L. Shrier ; Alex Pentland 出版社：Mit Pr 裝訂：平裝

How to create an Internet of Trusted Data in which insights from data can be extracted without collecting, holding, or revealing the underlying data.Trusted Data describes a data architecture that pla

優惠:外文書周末優惠-單79雙75 優惠價： 79 901

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35.

All Data Are Local ― Thinking Critically in a Data-driven Society

出版日：2019/04/30 作者：Yanni Alexander Loukissas 出版社：Mit Pr 裝訂：精裝

How to analyze data settings rather than data sets, acknowledging the meaning-making power of the local.In our data-driven society, it is too easy to assume the transparency of data. Instead, Yanni Lo

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Machine Learning for Data Streams ― With Practical Examples in Moa

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36.

Machine Learning for Data Streams ― With Practical Examples in Moa

出版日：2018/03/02 作者：Albert Bifet; Gavald Ricard; Geoffrey Holmes; Bernhard Pfahringer 出版社：Mit Pr 裝訂：精裝

A hands-on approach to tasks and techniques in data stream mining and real-time analytics, with examples in MOA, a popular freely available open-source software framework.Today many information source

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37.

Big Data Is Not a Monolith

出版日：2016/10/21 作者：Cassidy R. Sugimoto; Hamid R. Ekbia; Michael Mattioli 出版社：Mit Pr 裝訂：精裝

Big data is ubiquitous but heterogeneous. Big data can be used to tally clicks and traffic on web pages, find patterns in stock trades, track consumer preferences, identi

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38.

Big Data Is Not a Monolith

出版日：2016/10/21 作者：Cassidy R. Sugimoto; Hamid R. Ekbia; Michael Mattioli 出版社：Mit Pr 裝訂：平裝

Big data is ubiquitous but heterogeneous. Big data can be used to tally clicks and traffic on web pages, find patterns in stock trades, track consumer preferences, identi

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39.

Raw Data Is an Oxymoron

出版日：2013/01/25 作者：Lisa Gitelman 出版社：Mit Pr 裝訂：平裝

We live in the era of Big Data, with storage and transmission capacity measured notjust in terabytes but in petabytes (where peta- denotes a quadrillion, or athousand trillion). Data collection is con

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40.

Semi-Supervised Learning

出版日：2010/01/22 作者：Olivier Chapelle; Bernhard Scholkopf; Alexander Zien 出版社：Mit Pr 裝訂：平裝

In the field of machine learning, semi-supervised learning (SSL) occupies the middle ground, between supervised learning (in which all training examples are labeled) and unsupervised learning (in whi

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熱搜：

Artificial Communication

Reality Mining ─ Using Big Data to Engineer a Better World

Data Science

Outer Limits of Reason

Human-Centered Data Science

Elements of Causal Inference ─ Foundations and Learning Algorithms

Machine Learning from Weak Supervision

Machine Learning, revised and updated edition

Machine Learners ― Archaeology of a Data Practice

Fundamentals of Machine Learning for Predictive Data Analytics ─ Algorithms, Worked Examples, and Case Studies

Introduction to Machine Learning

Machine Learning ─ A Probabilistic Perspective

Introduction to Machine Learning

Introduction To Machine Learning

High-Performance Big Data Computing

Machine Learners ─ Archaeology of a Data Practice

Big Data, Little Data, No Data ─ Scholarship in the Networked World

Big Data, Little Data, No Data ─ Scholarship in the Networked World

Optimization for Machine Learning

Variability and Consistency in Early Language Learning：The Wordbank Project

Fndls Of Machine Lrng Fo

Cultural Analytics

Building Ontologies With Basic Formal Ontology

Computational Finance 1999

Artful Science ─ Enlightenment Entertainment and the Eclipse of Visual Education

Data Action

An Experiential Introduction to Principles of Programming Languages

All Data Are Local: Thinking Critically in a Data-Driven Society

Persuading with Data

Probabilistic Machine Learning

Introduction to Modeling Cognitive Processes

Child Data Citizen

Data Action

Trusted Data ― A New Framework for Identity and Data Sharing

All Data Are Local ― Thinking Critically in a Data-driven Society

Machine Learning for Data Streams ― With Practical Examples in Moa

Big Data Is Not a Monolith

Big Data Is Not a Monolith

Raw Data Is an Oxymoron

Semi-Supervised Learning