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.

    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.

    K. Dewdney's The Turing Omnibus.Updated and expanded, The Turing Omnibus offers 66 concise, brilliantly written articles on the major points of interest in computer science theory, technology, and applications. New for this tour: updated information on algorithms, detecting primes, noncomputable functions, and self-replicating computers--plus completely new sections on the Mandelbrot set, genetic algorithms, the Newton-Raphson Method, neural networks that learn, DOS systems for personal computers, and computer viruses.Contents:1 Algorithms 2 Finite Automata 3 Systems of Logic 4 Simulation 5 Godel's Theorem 6 Game Trees 7 The Chomsky Hierarchy 8 Random Numbers 9 Mathematical Research 10 Program Correctness 11 Search Trees 12 Error-Corecting Codes 13 Boolean Logic 14 Regular Languages 15 Time and Space Complexity 16 Genetic Algorithms 17 The Random Access Machine 18 Spline Curves 19 Computer Vision 20 Karnaugh Maps 21 The Newton-Raphson Method 22 Minimum Spanning Trees 23 Generative Grammars 24 Recursion 25 Fast Multiplication 26 Nondeterminism 27 Perceptrons 28 Encoders and Multiplexers 29 CAT Scanning 30 The Partition Problem 31 Turing Machines 32 The Fast Fourier Transform 33 Analog Computing 34 Satisfiability 35 Sequential Sorting 36 Neural Networks That Learn 37 Public Key Cryptography 38 Sequential Cirucits 39 Noncomputerable Functions 40 Heaps and Merges 41 NP-Completeness 42 Number Systems for Computing 43 Storage by Hashing 44 Cellular Automata 45 Cook's Theorem 46 Self-Replicating Computers 47 Storing Images 48 The SCRAM 49 Shannon's Theory 50 Detecting Primes 51 Universal Turing Machines 52 Text Compression 53 Disk Operating Systems 54 NP-Complete Problems 55 Iteration and Recursion 56 VLSI Computers 57 Linear Programming 58 Predicate Calculus 59 The Halting Problem 60 Computer Viruses 61 Searching Strings 62 Parallel Computing 63 The Word Problem 64 Logic Programming 65 Relational Data Bases 66 Church's Thesis

    In this book, William Cook takes readers on a mathematical excursion, picking up the salesman's trail in the 1800s when Irish mathematician W. R. Hamilton first defined the problem, and venturing to the furthest limits of today's state-of-the-art attempts to solve it. He also explores its many important applications, from genome sequencing and designing computer processors to arranging music and hunting for planets.In Pursuit of the Traveling Salesman travels to the very threshold of our understanding about the nature of complexity, and challenges you yourself to discover the solution to this captivating mathematical problem.

    At the time we were hoping that our approach of writing a book which would be both accessible without mathematical sophistication and portray these exiting new fields in an authentic manner would find an audience. Now we know it did. We know from many reviews and personal letters that the book is used in a wide range of ways: researchers use it to acquaint themselves, teachers use it in college and university courses, students use it for background reading, and there is also a substantial audience of lay people who just want to know what chaos and fractals are about. Every book that is somewhat technical in nature is likely to have a number of misprints and errors in its first edition. Some of these were caught and brought to our attention by our readers. One of them, Hermann Flaschka, deserves to be thanked in particular for his suggestions and improvements. This second edition has several changes. We have taken out the two appendices from the firstedition. At the time of the first edition Yuval Fishers contribution, which we published as an appendix was probably the first complete expository account on fractal image compression. Meanwhile, Yuvals book Fractal Image Compression: Theory and Application appeared and is now the publication to refer to.

    With this long-awaited revision, the authors continue to present the theory in a concise and straightforward manner, now with an eye out for the practical applications. They have revised this book to make it more accessible to today's students, including the addition of more material on writing proofs, more figures and pictures to convey ideas, side-boxes to highlight other interesting material, and a less formal writing style. Exercises at the end of each chapter, including some new, easier exercises, help readers confirm and enhance their understanding of the material. *NEW! Completely rewritten to be less formal, providing more accessibility to todays students. *NEW! Increased usage of figures and pictures to help convey ideas. *NEW! More detail and intuition provided for definitions and proofs. *NEW! Provides special side-boxes to present supplemental material that may be of interest to readers. *NEW! Includes more exercises, including many at a lower level. *NEW! Presents program-like notation for PDAs and Turing machines. *NEW! Increas

    This book provides an introduction to the field of solid state physics for undergraduate students in physics, chemistry, engineering, and materials science.

     They are also used in countless industries such as medicine, manufacturing and finance to help companies make better decisions and reduce risk. Whether coded or scratched out by hand, both algorithms are powerful tools that can make a significant impact. This book is a visual introduction for beginners that unpacks the fundamentals of decision trees and random forests. If you want to dig into the basics with a visual twist plus create your own machine learning algorithms in Python, this book is for you.

    A comprehensive introduction to the subject, this book shows in detail how such problems can be solved numerically with great efficiency. The focus is on recognizing convex optimization problems and then finding the most appropriate technique for solving them. The text contains many worked examples and homework exercises and will appeal to students, researchers and practitioners in fields such as engineering, computer science, mathematics, statistics, finance, and economics.

    Mitchell covers the field of machine learning, the study of algorithms that allow computer programs to automatically improve through experience and that automatically infer general laws from specific data.

    Beginning with an introduction to array formulas, this manual examines topics such as how they differ from ordinary formulas, the benefits and drawbacks of their use, functions that can and cannot handle array calculations, and array constants and functions. Among the practical applications surveyed include how to extract data from tables and unique lists, how to get results that match any criteria, and how to utilize various methods for unique counts. This book contains 529 screen shots.

    The framework of probabilistic graphical models, presented in this book, provides a general approach for this task. The approach is model-based, allowing interpretable models to be constructed and then manipulated by reasoning algorithms. These models can also be learned automatically from data, allowing the approach to be used in cases where manually constructing a model is difficult or even impossible. Because uncertainty is an inescapable aspect of most real-world applications, the book focuses on probabilistic models, which make the uncertainty explicit and provide models that are more faithful to reality.Probabilistic Graphical Models discusses a variety of models, spanning Bayesian networks, undirected Markov networks, discrete and continuous models, and extensions to deal with dynamical systems and relational data. For each class of models, the text describes the three fundamental cornerstones: representation, inference, and learning, presenting both basic concepts and advanced techniques. Finally, the book considers the use of the proposed framework for causal reasoning and decision making under uncertainty. The main text in each chapter provides the detailed technical development of the key ideas. Most chapters also include boxes with additional material: skill boxes, which describe techniques; case study boxes, which discuss empirical cases related to the approach described in the text, including applications in computer vision, robotics, natural language understanding, and computational biology; and concept boxes, which present significant concepts drawn from the material in the chapter. Instructors (and readers) can group chapters in various combinations, from core topics to more technically advanced material, to suit their particular needs.

    This introduction is ideal for beginners: it requires no previous programming experience and all concepts are explained from first principles via carefully chosen examples. Each chapter includes exercises that range from the straightforward to extended projects, plus suggestions for further reading on more advanced topics. The author is a leading Haskell researcher and instructor, well-known for his teaching skills. The presentation is clear and simple, and benefits from having been refined and class-tested over several years. The result is a text that can be used with courses, or for self-learning. Features include freely accessible Powerpoint slides for each chapter, solutions to exercises and examination questions (with solutions) available to instructors, and a downloadable code that's fully compliant with the latest Haskell release.

    The long-anticipated revision of this best-selling text offers the most comprehensive, up-to-date introduction to the theory and practice of artificial intelligence. *NEW-Nontechnical learning material-Accompanies each part of the book. *NEW-The Internet as a sample application for intelligent systems-Added in several places including logical agents, planning, and natural language. *NEW-Increased coverage of material - Includes expanded coverage of: default reasoning and truth maintenance systems, including multi-agent/distributed AI and game theory; probabilistic approaches to learning including EM; more detailed descriptions of probabilistic inference algorithms. *NEW-Updated and expanded exercises-75% of the exercises are revised, with 100 new exercises. *NEW-On-line Java software. *Makes it easy for students to do projects on the web using intelligent agents. *A unified, agent-based approach to AI-Organizes the material around the task of building intelligent agents. *Comprehensive, up-to-date coverage-Includes a unified view of the field organized around the rational decision making pa

    During that week, you will learn almost everything modern machine learning has to offer. The author and other practitioners have spent years learning these concepts.Companion wiki — the book has a continuously updated wiki that extends some book chapters with additional information: Q&A, code snippets, further reading, tools, and other relevant resources.Flexible price and formats — choose from a variety of formats and price options: Kindle, hardcover, paperback, EPUB, PDF. If you buy an EPUB or a PDF, you decide the price you pay!Read first, buy later — download book chapters for free, read them and share with your friends and colleagues. Only if you liked the book or found it useful in your work, study or business, then buy it.