Many successful applications of machine learning exist already, including systems that analyze past sales data to predict customer behavior, recognize faces or spoken speech, optimize robot behavior so that a task can be completed using minimum resources, and extract knowledge from bioinformatics data. "Introduction to Machine Learning" is a comprehensive textbook on the subject, covering a broad array of topics not usually included in introductory machine learning texts. It discusses many methods based in different fields, including statistics, pattern recognition, neural networks, artificial intelligence, signal processing, control, and data mining, in order to present a unified treatment of machine learning problems and solutions. All learning algorithms are explained so that the student can easily move from the equations in the book to a computer program. The book can be used by advanced undergraduates and graduate students who have completed courses in computer programming, probability, calculus, and linear algebra. It will also be of interest to engineers in the field who are concerned with the application of machine learning methods.After an introduction that defines machine learning and gives examples of machine learning applications, the book covers supervised learning, Bayesian decision theory, parametric methods, multivariate methods, dimensionality reduction, clustering, nonparametric methods, decision trees, linear discrimination, multilayer perceptrons, local models, hidden Markov models, assessing and comparing classification algorithms, combining multiple learners, and reinforcement learning.

    

    Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach.The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic methods, the book stresses a principled model-based approach, often using the language of graphical models to specify models in a concise and intuitive way. Almost all the models described have been implemented in a MATLAB software package—PMTK (probabilistic modeling toolkit)—that is freely available online. The book is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students.

    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.

    These days, linger over a Web page selling lamps, and they will turn up at the advertising margins as you move around the Internet, reminding you, tempting you to make that purchase. Search engines such as Google can now look deep into the data on the Web to pull out instances of the words you are looking for. And there are pages that collect and assess information to give you a snapshot of changing political opinion. These are just basic examples of the growth of Web intelligence, as increasingly sophisticated algorithms operate on the vast and growing amount of data on the Web, sifting, selecting, comparing, aggregating, correcting; following simple but powerful rules to decide what matters. While original optimism for Artificial Intelligence declined, this new kind of machine intelligence is emerging as the Web grows ever larger and more interconnected.Gautam Shroff takes us on a journey through the computer science of search, natural language, text mining, machine learning, swarm computing, and semantic reasoning, from Watson to self-driving cars. This machine intelligence may even mimic at a basic level what happens in the brain.

    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.

    It is suitable for a one or two semester university course on AI, as well as for researchers in the field.

    It’s about the greatest pirate in history, the most powerful executive in the music business, a revolutionary invention and an illegal website four times the size of the iTunes Music Store. Journalist Stephen Witt traces the secret history of digital music piracy, from the German audio engineers who invented the mp3, to a North Carolina compact-disc manufacturing plant where factory worker Dell Glover leaked nearly two thousand albums over the course of a decade, to the high-rises of midtown Manhattan where music executive Doug Morris cornered the global market on rap, and, finally, into the darkest recesses of the Internet.Through these interwoven narratives, Witt has written a thrilling book that depicts the moment in history when ordinary life became forever entwined with the world online — when, suddenly, all the music ever recorded was available for free. In the page-turning tradition of writers like Michael Lewis and Lawrence Wright, Witt’s deeply-reported first book introduces the unforgettable characters—inventors, executives, factory workers, and smugglers—who revolutionized an entire artform, and reveals for the first time the secret underworld of media pirates that transformed our digital lives.An irresistible never-before-told story of greed, cunning, genius, and deceit, How Music Got Free isn’t just a story of the music industry—it’s a must-read history of the Internet itself.

    It is also a practical, modern introduction to scientific computing in Python, tailored for data-intensive applications. This is a book about the parts of the Python language and libraries you'll need to effectively solve a broad set of data analysis problems. This book is not an exposition on analytical methods using Python as the implementation language.Written by Wes McKinney, the main author of the pandas library, this hands-on book is packed with practical cases studies. It's ideal for analysts new to Python and for Python programmers new to scientific computing.Use the IPython interactive shell as your primary development environmentLearn basic and advanced NumPy (Numerical Python) featuresGet started with data analysis tools in the pandas libraryUse high-performance tools to load, clean, transform, merge, and reshape dataCreate scatter plots and static or interactive visualizations with matplotlibApply the pandas groupby facility to slice, dice, and summarize datasetsMeasure data by points in time, whether it's specific instances, fixed periods, or intervalsLearn how to solve problems in web analytics, social sciences, finance, and economics, through detailed examples

    A step-by-step visual journey through the mathematics of neural networks, and making your own using Python and Tensorflow.

    He argues that the anthropocene - the age in which humans acquired planetary-scale technologies - is, after 300 years, coming to an end. A new age - the novacene - has already begun.New beings will emerge from existing artificial intelligence systems. They will think 10,000 times faster than we do and they will regard us as we now regard plants - as desperately slow acting and thinking creatures. But this will not be the cruel, violent machine takeover of the planet imagined by sci-fi writers and film-makers. These hyper-intelligent beings will be as dependent on the health of the planet as we are. They will need the planetary cooling system of Gaia to defend them from the increasing heat of the sun as much as we do. And Gaia depends on organic life. We will be partners in this project.It is crucial, Lovelock argues, that the intelligence of Earth survives and prospers. He does not think there are intelligent aliens, so we are the only beings capable of understanding the cosmos. Maybe, he speculates, the novacene could even be the beginning of a process that will finally lead to intelligence suffusing the entire cosmos. At the age 100, James Lovelock has produced the most important and compelling work of his life.

     Once one of Silicon Valley’s greatest success stories, Facebook has been under constant fire for the past five years, roiled by controversies and crises. It turns out that while the tech giant was connecting the world, they were also mishandling users’ data, spreading fake news, and amplifying dangerous, polarizing hate speech. The company, many said, had simply lost its way. But the truth is far more complex. Leadership decisions enabled, and then attempted to deflect attention from, the crises. Time after time, Facebook’s engineers were instructed to create tools that encouraged people to spend as much time on the platform as possible, even as those same tools boosted inflammatory rhetoric, conspiracy theories, and partisan filter bubbles. And while consumers and lawmakers focused their outrage on privacy breaches and misinformation, Facebook solidified its role as the world’s most voracious data-mining machine, posting record profits, and shoring up its dominance via aggressive lobbying efforts. Drawing on their unrivaled sources, Sheera Frenkel and Cecilia Kang take readers inside the complex court politics, alliances and rivalries within the company to shine a light on the fatal cracks in the architecture of the tech behemoth. Their explosive, exclusive reporting led them to a shocking conclusion: The missteps of the last five years were not an anomaly but an inevitability—this is how Facebook was built to perform. In a period of great upheaval, growth has remained the one constant under the leadership of Mark Zuckerberg and Sheryl Sandberg. Both have been held up as archetypes of uniquely 21st century executives—he the tech “boy genius” turned billionaire, she the ultimate woman in business, an inspiration to millions through her books and speeches. But sealed off in tight circles of advisers and hobbled by their own ambition and hubris, each has stood by as their technology is coopted by hate-mongers, criminals and corrupt political regimes across the globe, with devastating consequences. In An Ugly Truth, they are at last held accountable.

    Courses and books on basic statistics rarely cover the topic from a data science perspective. This practical guide explains how to apply various statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what's important and what's not.Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If you're familiar with the R programming language, and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format.With this book, you'll learn:Why exploratory data analysis is a key preliminary step in data scienceHow random sampling can reduce bias and yield a higher quality dataset, even with big dataHow the principles of experimental design yield definitive answers to questionsHow to use regression to estimate outcomes and detect anomaliesKey classification techniques for predicting which categories a record belongs toStatistical machine learning methods that "learn" from dataUnsupervised learning methods for extracting meaning from unlabeled data

    But as the gaming industry grows in numerous directions and everyone talks about the advance of the moment, few explore and seek to understand the forces behind this profound evolution. How did we get from Space Invaders to Grand Theft Auto? How exactly did gaming become a $50 billion industry and a dominant pop culture form? What are the stories, the people, the innovations, and the fascinations behind this incredible growth?Through extensive interviews with gaming's greatest innovators, both its icons and those unfairly forgotten by history, All Your Base Are Belong To Us sets out to answer these questions, exposing the creativity, odd theories--and passion--behind the twenty-first century's fastest-growing medium.Go inside the creation of: Grand Theft Auto * World of Warcraft * Bioshock * Kings Quest * Bejeweled * Madden Football * Super Mario Brothers * Myst * Pong * Donkey Kong * Crash Bandicoot * The 7th Guest * Tetris * Shadow Complex * Everquest * The Sims * And many more!

    Pure Substances, The First And Second Laws, Gases, Psychrometrics, The Vapor, Gas And Refrigeration Cycles, Heat Transfer, Compressible Flow, Chemical Reactions, Fuels, And More Are Presented In Detail And Enhanced With Practical Applications. This Version Presents The Material Using SI Units And Has Ample Material On SI Conversion, Steam Tables, And A Mollier Diagram. A CD-ROM, Included With The Print Version Of The Text, Includes A Fully Functional Version Of Quickfield (Widely Used In Industry), As Well As Numerous Demonstrations And Simulations With MATLAB, And Other Third Party Software.