A First Course in Machine Learning, Rogers Simon, Girolami Mark
Автор: Strang Gilbert Название: Linear Algebra and Learning from Data ISBN: 0692196382 ISBN-13(EAN): 9780692196380 Издательство: Cambridge Academ Рейтинг: Цена: 9978.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Linear algebra and the foundations of deep learning, together at last! From Professor Gilbert Strang, acclaimed author of Introduction to Linear Algebra, comes Linear Algebra and Learning from Data, the first textbook that teaches linear algebra together with deep learning and neural nets. This readable yet rigorous textbook contains a complete course in the linear algebra and related mathematics that students need to know to get to grips with learning from data. Included are: the four fundamental subspaces, singular value decompositions, special matrices, large matrix computation techniques, compressed sensing, probability and statistics, optimization, the architecture of neural nets, stochastic gradient descent and backpropagation.
Автор: Levesque Hector J. Название: Thinking as Computation: A First Course ISBN: 0262016990 ISBN-13(EAN): 9780262016995 Издательство: MIT Press Рейтинг: Цена: 4967.00 р. Наличие на складе: Нет в наличии.
Описание: Students explore the idea that thinking is a form of computation by learning to write simple computer programs for tasks that require thought.
Автор: Loveday Название: A First Course in Statistics ISBN: 1316607003 ISBN-13(EAN): 9781316607008 Издательство: Cambridge Academ Рейтинг: Цена: 3958.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Originally published in 1958, this informative textbook is the first part of a two-volume set, which explores the subject of statistics in full, from elementary to advanced level. Primarily aimed at school students as a course of self-study, this first part focuses on the elementary and contains multiple examples and exercises.
Автор: Comrey Название: A First Course in Factor Analysis ISBN: 1138965456 ISBN-13(EAN): 9781138965454 Издательство: Taylor&Francis Рейтинг: Цена: 7654.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
The goal of this book is to foster a basic understanding of factor analytic techniques so that readers can use them in their own research and critically evaluate their use by other researchers. Both the underlying theory and correct application are emphasized. The theory is presented through the mathematical basis of the most common factor analytic models and several methods used in factor analysis. On the application side, considerable attention is given to the extraction problem, the rotation problem, and the interpretation of factor analytic results. Hence, readers are given a background of understanding in the the theory underlying factor analysis and then taken through the steps in executing a proper analysis -- from the initial problem of design through choice of correlation coefficient, factor extraction, factor rotation, factor interpretation, and writing up results.
This revised edition includes introductions to newer methods -- such as confirmatory factor analysis and structural equation modeling -- that have revolutionized factor analysis in recent years. To help remove some of the mystery underlying these newer, more complex methods, the introductory examples utilize EQS and LISREL. Updated material relating to the validation of the Comrey Personality Scales also has been added. Finally, program disks for running factor analyses on either an IBM-compatible PC or a mainframe with FORTRAN capabilities are available. The intended audience for this volume includes talented but mathematically unsophisticated advanced undergraduates, graduate students, and research workers seeking to acquire a basic understanding of the principles supporting factor analysis. Disks are available in 5.25 and 3.5 formats for both mainframe programs written in Fortran and IBM PCs and compatibles running a math co-processor.
Автор: Balan Raluca Название: Expect The Unexpected: A First Course In Biostatistics ISBN: 9813209054 ISBN-13(EAN): 9789813209053 Издательство: World Scientific Publishing Рейтинг: Цена: 11563.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This textbook introduces the basic concepts from probability theory and statistics which are needed for statistical analysis of data encountered in the biological and health sciences. No previous study is required. Advanced mathematical tools, such as integration and differentiation, are kept to a minimum. The emphasis is put on the examples. Probabilistic methods are discussed at length, but the focus of this edition is on statistics.The examples are kept simple, so that the reader can learn quickly and see the usefulness of various statistical and probabilistic methods. Some of the examples used in this book draw attention to various problems related to environmental issues, climate change, loss of bio-diversity, and their impact on wildlife and humans.In comparison with the first edition of the book, this second edition contains additional topics such as power, sample size computation and non-parametric methods, and includes a large collection of new problems, as well as the answers to odd-numbered problems. Several sections of this edition are accompanied by instructions using the programming language R for statistical computing and graphics.The Solution Manual is available upon request for all instructors who adopt this book as a course text. Please send your request to sales@wspc.com.
Автор: Bernard Flury Название: A First Course in Multivariate Statistics ISBN: 1441931139 ISBN-13(EAN): 9781441931139 Издательство: Springer Рейтинг: Цена: 13409.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: My goal in writing this book has been to provide teachers and students of multi- variate statistics with a unified treatment ofboth theoretical and practical aspects of this fascinating area. The text is designed for a broad readership, including advanced undergraduate students and graduate students in statistics, graduate students in bi- ology, anthropology, life sciences, and other areas, and postgraduate students. The style of this book reflects my beliefthat the common distinction between multivariate statistical theory and multivariate methods is artificial and should be abandoned. I hope that readers who are mostly interested in practical applications will find the theory accessible and interesting. Similarly I hope to show to more mathematically interested students that multivariate statistical modelling is much more than applying formulas to data sets. The text covers mostly parametric models, but gives brief introductions to computer-intensive methods such as the bootstrap and randomization tests as well. The selection of material reflects my own preferences and views. My principle in writing this text has been to restrict the presentation to relatively few topics, but cover these in detail. This should allow the student to study an area deeply enough to feel comfortable with it, and to start reading more advanced books or articles on the same topic.
Описание: A comprehensive textbook for undergraduate courses in introductory probability, offering a case-study approach, with examples from engineering and the social and life sciences.
Data Mining: Practical Machine Learning Tools and Techniques, Fourth Edition, offers a thorough grounding in machine learning concepts, along with practical advice on applying these tools and techniques in real-world data mining situations. This highly anticipated fourth edition of the most acclaimed work on data mining and machine learning teaches readers everything they need to know to get going, from preparing inputs, interpreting outputs, evaluating results, to the algorithmic methods at the heart of successful data mining approaches.
Extensive updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including substantial new chapters on probabilistic methods and on deep learning. Accompanying the book is a new version of the popular WEKA machine learning software from the University of Waikato. Authors Witten, Frank, Hall, and Pal include today's techniques coupled with the methods at the leading edge of contemporary research.
Please visit the book companion website at https: //www.cs.waikato.ac.nz/ ml/weka/book.html.
It contains
Powerpoint slides for Chapters 1-12. This is a very comprehensive teaching resource, with many PPT slides covering each chapter of the book
Online Appendix on the Weka workbench; again a very comprehensive learning aid for the open source software that goes with the book
Table of contents, highlighting the many new sections in the 4th edition, along with reviews of the 1st edition, errata, etc.
Provides a thorough grounding in machine learning concepts, as well as practical advice on applying the tools and techniques to data mining projects
Presents concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methods
Includes a downloadable WEKA software toolkit, a comprehensive collection of machine learning algorithms for data mining tasks-in an easy-to-use interactive interface
Includes open-access online courses that introduce practical applications of the material in the book
Автор: Bradley Efron and Trevor Hastie Название: Computer Age Statistical Inference ISBN: 1107149894 ISBN-13(EAN): 9781107149892 Издательство: Cambridge Academ Рейтинг: Цена: 9029.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The twenty-first century has seen a breathtaking expansion of statistical methodology, both in scope and in influence. 'Big data', 'data science', and 'machine learning' have become familiar terms in the news, as statistical methods are brought to bear upon the enormous data sets of modern science and commerce. How did we get here? And where are we going? This book takes us on an exhilarating journey through the revolution in data analysis following the introduction of electronic computation in the 1950s. Beginning with classical inferential theories - Bayesian, frequentist, Fisherian - individual chapters take up a series of influential topics: survival analysis, logistic regression, empirical Bayes, the jackknife and bootstrap, random forests, neural networks, Markov chain Monte Carlo, inference after model selection, and dozens more. The distinctly modern approach integrates methodology and algorithms with statistical inference. The book ends with speculation on the future direction of statistics and data science.
Автор: Raschka, Sebastian Mirjalili, Vahid Название: Python machine learning - ISBN: 1787125939 ISBN-13(EAN): 9781787125933 Издательство: Неизвестно Цена: 8091.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This second edition of Python Machine Learning by Sebastian Raschka is for developers and data scientists looking for a practical approach to machine learning and deep learning. In this updated edition, you`ll explore the machine learning process using Python and the latest open source technologies, including scikit-learn and TensorFlow 1.x.
Автор: Rogers Название: A First Course in Machine Learning, Second Edition ISBN: 1498738486 ISBN-13(EAN): 9781498738484 Издательство: Taylor&Francis Рейтинг: Цена: 10564.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The new edition of this popular, undergraduate textbook has been revised and updated to reflect current growth areas in Machine Learning. The new edition includes three new chapters with more detailed discussion of Markov Chain Monte Carlo techniques, Classification and Regression with Gaussian Processes, and Dirichlet Process models.
Автор: Kai Lai Chung Название: A Course in Probability Theory, Revised Edition, ISBN: 0121741516 ISBN-13(EAN): 9780121741518 Издательство: Elsevier Science Рейтинг: Цена: 12462.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book is designed for undergraduate programs and students and can also be used as a first-year graduate text in probability. It offers a broad perspective, building on the synopsis of measure and integration offered in Chapter two.
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