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.

    

    Building on the successful top-down approach of previous editions, this fourth edition continues with an early emphasis on application-layer paradigms and application programming interfaces, encouraging a hands-on experience with protocols and networking concepts.

    Designed primarily for undergraduates, this self-help manual offers straightforward solutions to common problems and an overview of the diversity of writing tasks faced by professional biologists.

    Covering all areas of language which students at this level find difficult, this substantially revised and updated book retains the clarity, simplicity and accessibility of the first edition, adding to it new and redesigned units and appendices, modified right-hand page exercises and additional exercises. - Easy to use: 136 two-page units combine clear, accurate language presentation on left-hand pages with thorough, varied practice on facing pages. - New additional exercises offer further practice of grammar points from different groups of units. - Designed for self-study: learners choose and study problematic areas with the help of a new study guide. - Key section contains answers to all exercises and the study guide. - Appendices deal with irregular verbs, tense formation, modals, spelling, short forms and American English.

    Get ready to amaze your friends—and yourself—with incredible calculations you never thought you could master, as renowned “mathemagician” Arthur Benjamin shares his techniques for lightning-quick calculations and amazing number tricks. This book will teach you to do math in your head faster than you ever thought possible, dramatically improve your memory for numbers, and—maybe for the first time—make mathematics fun.Yes, even you can learn to do seemingly complex equations in your head; all you need to learn are a few tricks. You’ll be able to quickly multiply and divide triple digits, compute with fractions, and determine squares, cubes, and roots without blinking an eye. No matter what your age or current math ability, Secrets of Mental Math will allow you to perform fantastic feats of the mind effortlessly. This is the math they never taught you in school.Also available as an eBook

    As a flight director in NASA’s Mission Control, Kranz witnessed firsthand the making of history. He participated in the space program from the early days of the Mercury program to the last Apollo mission, and beyond. He endured the disastrous first years when rockets blew up and the United States seemed to fall further behind the Soviet Union in the space race. He helped to launch Alan Shepard and John Glenn, then assumed the flight director’s role in the Gemini program, which he guided to fruition. With his teammates, he accepted the challenge to carry out President John F. Kennedy’s commitment to land a man on the Moon before the end of the 1960s. Kranz recounts these thrilling historic events and offers new information about the famous flights. What appeared as nearly flawless missions to the Moon were, in fact, a series of hair-raising near misses. When the space technology failed, as it sometimes did, the controllers’ only recourse was to rely on their skills and those of their teammates. He reveals behind-the-scenes details to demonstrate the leadership, discipline, trust, and teamwork that made the space program a success. A fascinating firsthand account by a veteran mission controller of one of America’s greatest achievements, Failure is Not an Option reflects on what has happened to the space program and offers his own bold suggestions about what we ought to be doing in space now.

    This manual, written by three practicing occultists, reveals magickal and alchemical methods, many which have been lost and suppressed through the ages, in a refreshingly modern way that magickal practitioners of any tradition can understand.

    It combines the essential elements of the theory with the practical applications. Containing many examples and problems with step-by-step solutions, this cleverly structured text assists the reader in mastering the machinery of quantum mechanics. * A comprehensive introduction to the subject * Includes over 65 solved examples integrated throughout the text * Includes over 154 fully solved multipart problems * Offers an indepth treatment of the practical mathematical tools of quantum mechanics * Accessible to teachers as well as students

    Leon McBeth's 'The Baptist heritage' is a definitive, fresh interpretation of Baptist history. Based on primary source research, the book combines the best features of chronological and topical history to bring alive the story of Baptists around the world.

    Their discussion ranges from the history of the field's intellectual foundations to the most recent developments and applications.Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives when interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the key ideas and algorithms of reinforcement learning. Their discussion ranges from the history of the field's intellectual foundations to the most recent developments and applications. The only necessary mathematical background is familiarity with elementary concepts of probability.The book is divided into three parts. Part I defines the reinforcement learning problem in terms of Markov decision processes. Part II provides basic solution methods: dynamic programming, Monte Carlo methods, and temporal-difference learning. Part III presents a unified view of the solution methods and incorporates artificial neural networks, eligibility traces, and planning; the two final chapters present case studies and consider the future of reinforcement learning.

    The Thinking Pilot guides you deeply into topics that weren't taught in flight training-everything from how to really do a preflight, through keeping your passengers happy, scud running, precautionary landings, and how to survive a crash. It includes a detailed introduction to flying floats, skis, aerobatics, and classic airplanes; probes some of aviation's dirty little secrets, explodes myths, and presents the best, most succinct guide to flying tailwheel airplanes ever written. Rick Durden was once described as aviation's Renaissance Man. He is an Airline Transport-rated pilot with experience in some 200 types of airplanes, a practicing aviation attorney who has been involved in hundreds of aircraft accident cases, writer, aviation magazine editor, safety counselor, flight instructor, volunteer pilot in remote areas of the U.S. and Central America, and has been the executive director of a nonprofit conservation organization making use of aircraft and volunteer pilots throughout much of North America.

    This clear, concise look at the basic principles and concepts of genetics uses a human genetics perspective to discuss the methods and experiments upon which genetic principles are based, such as DNA replication.

    When Arsene Wenger said he wanted his team to go through a season undefeated, he was roundly mocked, yet just over 12 months later the Arsenal manager guided his team to footballing immortality.The Gunners’ 2003-2004 campaign was extraordinary, and not just because of their remarkable league achievement.From the highs and lows of Europe, to disciplinary charges, pitched battles, the frantic scramble to finance a new stadium, a club record transfer in January, and winning the title at the ground of the old enemy, this special collector’s edition book looks back on everything that happened during an unforgettable campaign.Match reports, behind the scenes stories, exclusive interviews and illustrations, stats, tactics, pictures; all the things you know, and lots of stuff you’ve forgotten: Together tells the story of Arsenal’s ‘unbeaten’ season.

    However, these activities can be viewed as two facets of the same field, and together they have undergone substantial development over the past ten years. In particular, 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 models. Also, the practical applicability of Bayesian methods has been greatly enhanced through the development of a range of approximate inference algorithms such as variational Bayes and expectation propagation. Similarly, new models based on kernels have had a significant impact on both algorithms and applications. This 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, and assumes no previous knowledge of pattern recognition or machine learning concepts. Knowledge of multivariate calculus and basic linear algebra is required, and some familiarity with probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.