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Data Science (The MIT Press Essential Knowledge)
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 science is to improve decision making through the analysis of data. Today data science determines the ads we see online, the books and movies that are recommended to us online, which emails are filtered into our spam folders, and even how much we pay for health insurance. This volume in the MIT Press Essential Knowledge series offers a concise introduction to the emerging field of data science, explaining its evolution, current uses, data infrastructure issues, and ethical challenges.It has never been easier for organizations to gather, store, and process data. Use of data science is driven by the rise of big data and social media, the development of high-performance computing, and the emergence of such powerful methods for data analysis and modeling as deep learning. Data science encompasses a set of principles, problem definitions, algorithms, and processes for extracting non-obvious and useful patterns from large datasets. It is closely related to the fields of data mining and machine learning, but broader in scope. This book offers a brief history of the field, introduces fundamental data concepts, and describes the stages in a data science project. It considers data infrastructure and the challenges posed by integrating data from multiple sources, introduces the basics of machine learning, and discusses how to link machine learning expertise with real-world problems. The book also reviews ethical and legal issues, developments in data regulation, and computational approaches to preserving privacy. Finally, it considers the future impact of data science and offers principles for success in data science projects.
Published by: The MIT Press | Publication date: 04/06/2018
Kindle book details: Kindle Edition, 282 pages

Post-Truth (Mit Press Essential Knowledge)
How we arrived in a post-truth era, when “alternative facts” replace actual facts, and feelings have more weight than evidence.Are we living in a post-truth world, where “alternative facts” replace actual facts and feelings have more weight than evidence? How did we get here? In this volume in the MIT Press Essential Knowledge series, Lee McIntyre traces the development of the post-truth phenomenon from science denial through the rise of “fake news,” from our psychological blind spots to the public's retreat into “information silos.”What, exactly, is post-truth? Is it wishful thinking, political spin, mass delusion, bold-faced lying? McIntyre analyzes recent examples—claims about inauguration crowd size, crime statistics, and the popular vote—and finds that post-truth is an assertion of ideological supremacy by which its practitioners try to compel someone to believe something regardless of the evidence. Yet post-truth didn't begin with the 2016 election; the denial of scientific facts about smoking, evolution, vaccines, and climate change offers a road map for more widespread fact denial. Add to this the wired-in cognitive biases that make us feel that our conclusions are based on good reasoning even when they are not, the decline of traditional media and the rise of social media, and the emergence of fake news as a political tool, and we have the ideal conditions for post-truth. McIntyre also argues provocatively that the right wing borrowed from postmodernism—specifically, the idea that there is no such thing as objective truth—in its attacks on science and facts.McIntyre argues that we can fight post-truth, and that the first step in fighting post-truth is to understand it.
Author: Lee McIntyre
Published by: The MIT Press | Publication date: 02/09/2018
Kindle book details: Kindle Edition, 240 pages

Introduction to Computation and Programming Using Python: With Application to Understanding Data (The MIT Press)
The new edition of an introductory text that teaches students the art of computational problem solving, covering topics ranging from simple algorithms to information visualization.This book introduces students with little or no prior programming experience to the art of computational problem solving using Python and various Python libraries, including PyLab. It provides students with skills that will enable them to make productive use of computational techniques, including some of the tools and techniques of data science for using computation to model and interpret data. The book is based on an MIT course (which became the most popular course offered through MIT's OpenCourseWare) and was developed for use not only in a conventional classroom but in in a massive open online course (MOOC). This new edition has been updated for Python 3, reorganized to make it easier to use for courses that cover only a subset of the material, and offers additional material including five new chapters.Students are introduced to Python and the basics of programming in the context of such computational concepts and techniques as exhaustive enumeration, bisection search, and efficient approximation algorithms. Although it covers such traditional topics as computational complexity and simple algorithms, the book focuses on a wide range of topics not found in most introductory texts, including information visualization, simulations to model randomness, computational techniques to understand data, and statistical techniques that inform (and misinform) as well as two related but relatively advanced topics: optimization problems and dynamic programming. This edition offers expanded material on statistics and machine learning and new chapters on Frequentist and Bayesian statistics.
Published by: The MIT Press | Publication date: 08/08/2016
Kindle book details: Kindle Edition, 472 pages

Introduction to Algorithms (The MIT Press)
A new edition of the essential text and professional reference, with substantial new material on such topics as vEB trees, multithreaded algorithms, dynamic programming, and edge-based flow.Some books on algorithms are rigorous but incomplete; others cover masses of material but lack rigor. Introduction to Algorithms uniquely combines rigor and comprehensiveness. The book covers a broad range of algorithms in depth, yet makes their design and analysis accessible to all levels of readers. Each chapter is relatively self-contained and can be used as a unit of study. The algorithms are described in English and in a pseudocode designed to be readable by anyone who has done a little programming. The explanations have been kept elementary without sacrificing depth of coverage or mathematical rigor.The first edition became a widely used text in universities worldwide as well as the standard reference for professionals. The second edition featured new chapters on the role of algorithms, probabilistic analysis and randomized algorithms, and linear programming. The third edition has been revised and updated throughout. It includes two completely new chapters, on van Emde Boas trees and multithreaded algorithms, substantial additions to the chapter on recurrence (now called “Divide-and-Conquer”), and an appendix on matrices. It features improved treatment of dynamic programming and greedy algorithms and a new notion of edge-based flow in the material on flow networks. Many new exercises and problems have been added for this edition. The international paperback edition is no longer available; the hardcover is available worldwide.
Published by: The MIT Press | Publication date: 07/31/2009
Kindle book details: Kindle Edition, 1312 pages

The Technological Singularity (The MIT Press Essential Knowledge series)
The idea of technological singularity, and what it would mean if ordinary human intelligence were enhanced or overtaken by artificial intelligence.The idea that human history is approaching a “singularity”—that ordinary humans will someday be overtaken by artificially intelligent machines or cognitively enhanced biological intelligence, or both—has moved from the realm of science fiction to serious debate. Some singularity theorists predict that if the field of artificial intelligence (AI) continues to develop at its current dizzying rate, the singularity could come about in the middle of the present century. Murray Shanahan offers an introduction to the idea of the singularity and considers the ramifications of such a potentially seismic event.Shanahan's aim is not to make predictions but rather to investigate a range of scenarios. Whether we believe that singularity is near or far, likely or impossible, apocalypse or utopia, the very idea raises crucial philosophical and pragmatic questions, forcing us to think seriously about what we want as a species. Shanahan describes technological advances in AI, both biologically inspired and engineered from scratch. Once human-level AI—theoretically possible, but difficult to accomplish—has been achieved, he explains, the transition to superintelligent AI could be very rapid. Shanahan considers what the existence of superintelligent machines could mean for such matters as personhood, responsibility, rights, and identity. Some superhuman AI agents might be created to benefit humankind; some might go rogue. (Is Siri the template, or HAL?) The singularity presents both an existential threat to humanity and an existential opportunity for humanity to transcend its limitations. Shanahan makes it clear that we need to imagine both possibilities if we want to bring about the better outcome.
Published by: The MIT Press | Publication date: 08/07/2015
Kindle book details: Kindle Edition, 274 pages

The Stuff of Bits: An Essay on the Materialities of Information (The MIT Press)
An argument that the material arrangements of information—how it is represented and interpreted—matter significantly for our experience of information and information systems.Virtual entities that populate our digital experience, like e-books, virtual worlds, and online stores, are backed by the large-scale physical infrastructures of server farms, fiber optic cables, power plants, and microwave links. But another domain of material constraints also shapes digital living: the digital representations sketched on whiteboards, encoded into software, stored in databases, loaded into computer memory, and transmitted on networks. These digital representations encode aspects of our everyday world and make them available for digital processing. The limits and capacities of those representations carry significant consequences for digital society. In The Stuff of Bits, Paul Dourish examines the specific materialities that certain digital objects exhibit. He presents four case studies: emulation, the creation of a “virtual” computer inside another; digital spreadsheets and their role in organizational practice; relational databases and the issue of “the databaseable”; and the evolution of digital networking and the representational entailments of network protocols. These case studies demonstrate how a materialist account can offer an entry point to broader concerns—questions of power, policy, and polity in the realm of the digital.
Author: Paul Dourish
Published by: The MIT Press | Publication date: 04/28/2017
Kindle book details: Kindle Edition, 256 pages

Energy and Civilization: A History (The MIT Press)
A comprehensive account of how energy has shaped society throughout history, from pre-agricultural foraging societies through today's fossil fuel–driven civilization."I wait for new Smil books the way some people wait for the next 'Star Wars' movie. In his latest book, Energy and Civilization: A History, he goes deep and broad to explain how innovations in humans' ability to turn energy into heat, light, and motion have been a driving force behind our cultural and economic progress over the past 10,000 years.—Bill Gates, Gates Notes, Best Books of the YearEnergy is the only universal currency; it is necessary for getting anything done. The conversion of energy on Earth ranges from terra-forming forces of plate tectonics to cumulative erosive effects of raindrops. Life on Earth depends on the photosynthetic conversion of solar energy into plant biomass. Humans have come to rely on many more energy flows—ranging from fossil fuels to photovoltaic generation of electricity—for their civilized existence. In this monumental history, Vaclav Smil provides a comprehensive account of how energy has shaped society, from pre-agricultural foraging societies through today's fossil fuel–driven civilization. Humans are the only species that can systematically harness energies outside their bodies, using the power of their intellect and an enormous variety of artifacts—from the simplest tools to internal combustion engines and nuclear reactors. The epochal transition to fossil fuels affected everything: agriculture, industry, transportation, weapons, communication, economics, urbanization, quality of life, politics, and the environment. Smil describes humanity's energy eras in panoramic and interdisciplinary fashion, offering readers a magisterial overview. This book is an extensively updated and expanded version of Smil's Energy in World History (1994). Smil has incorporated an enormous amount of new material, reflecting the dramatic developments in energy studies over the last two decades and his own research over that time.
Author: Vaclav Smil
Published by: The MIT Press | Publication date: 05/12/2017
Kindle book details: Kindle Edition, 568 pages

Neuroplasticity (The MIT Press Essential Knowledge series)
The real story of how our brains and nervous systems change throughout our lifetimes—with or without “brain training.”Fifty years ago, neuroscientists thought that a mature brain was fixed like a fly in amber, unable to change. Today, we know that our brains and nervous systems change throughout our lifetimes. This concept of neuroplasticity has captured the imagination of a public eager for self-improvement—and has inspired countless Internet entrepreneurs who peddle dubious “brain training” games and apps. In this book, Moheb Costandi offers a concise and engaging overview of neuroplasticity for the general reader, describing how our brains change continuously in response to our actions and experiences.Costandi discusses key experimental findings, and describes how our thinking about the brain has evolved over time. He explains how the brain changes during development, and the “synaptic pruning” that takes place before brain maturity. He shows that adult brains can grow new cells (citing, among many other studies, research showing that sexually mature male canaries learn a new song every year). He describes the kind of brain training that can bring about improvement in brain function. It's not gadgets and games that promise to “rewire your brain” but such sustained cognitive tasks as learning a musical instrument or a new language. (Costandi also notes that London cabbies increase their gray matter after rigorous training in their city's complicated streets.) He tells how brains compensate after stroke or injury; describes addiction and pain as maladaptive forms of neuroplasticity; and considers brain changes that accompany childhood, adolescence, parenthood, and aging. Each of our brains is custom-built. Neuroplasticity is at the heart of what makes us human.
Published by: The MIT Press | Publication date: 08/16/2016
Kindle book details: Kindle Edition, 194 pages

Computing: A Concise History (The MIT Press Essential Knowledge series)
A compact and accessible history, from punch cards and calculators to UNIVAC and ENIAC, the personal computer, Silicon Valley, and the Internet.The history of computing could be told as the story of hardware and software, or the story of the Internet, or the story of “smart” hand-held devices, with subplots involving IBM, Microsoft, Apple, Facebook, and Twitter. In this concise and accessible account of the invention and development of digital technology, computer historian Paul Ceruzzi offers a broader and more useful perspective. He identifies four major threads that run throughout all of computing's technological development: digitization—the coding of information, computation, and control in binary form, ones and zeros; the convergence of multiple streams of techniques, devices, and machines, yielding more than the sum of their parts; the steady advance of electronic technology, as characterized famously by “Moore's Law”; and the human-machine interface.Ceruzzi guides us through computing history, telling how a Bell Labs mathematician coined the word “digital” in 1942 (to describe a high-speed method of calculating used in anti-aircraft devices), and recounting the development of the punch card (for use in the 1890 U.S. Census). He describes the ENIAC, built for scientific and military applications; the UNIVAC, the first general purpose computer; and ARPANET, the Internet's precursor. Ceruzzi's account traces the world-changing evolution of the computer from a room-size ensemble of machinery to a “minicomputer” to a desktop computer to a pocket-sized smart phone. He describes the development of the silicon chip, which could store ever-increasing amounts of data and enabled ever-decreasing device size. He visits that hotbed of innovation, Silicon Valley, and brings the story up to the present with the Internet, the World Wide Web, and social networking.
Published by: The MIT Press | Publication date: 06/15/2012
Kindle book details: Kindle Edition, 199 pages

Machine Learning: The New AI (The MIT Press Essential Knowledge series)
A concise overview of machine learning—computer programs that learn from data—which underlies applications that include recommendation systems, face recognition, and driverless cars.Today, machine learning underlies a range of applications we use every day, from product recommendations to voice recognition—as well as some we don't yet use everyday, including driverless cars. It is the basis of the new approach in computing where we do not write programs but collect data; the idea is to learn the algorithms for the tasks automatically from data. As computing devices grow more ubiquitous, a larger part of our lives and work is recorded digitally, and as “Big Data” has gotten bigger, the theory of machine learning—the foundation of efforts to process that data into knowledge—has also advanced. In this book, machine learning expert Ethem Alpaydin offers a concise overview of the subject for the general reader, describing its evolution, explaining important learning algorithms, and presenting example applications.Alpaydin offers an account of how digital technology advanced from number-crunching mainframes to mobile devices, putting today's machine learning boom in context. He describes the basics of machine learning and some applications; the use of machine learning algorithms for pattern recognition; artificial neural networks inspired by the human brain; algorithms that learn associations between instances, with such applications as customer segmentation and learning recommendations; and reinforcement learning, when an autonomous agent learns act so as to maximize reward and minimize penalty. Alpaydin then considers some future directions for machine learning and the new field of “data science,” and discusses the ethical and legal implications for data privacy and security.
Published by: The MIT Press | Publication date: 09/30/2016
Kindle book details: Kindle Edition, 232 pages
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