# krainaksiazek machine learning and data science an introduction to statistical learning methods with r 20128185

- znaleziono 26 produktów w 3 sklepach

### Introduction To Statistical Machine Learning

**Książki Obcojęzyczne>Angielskie>Computing & information technology>Computer science>Artificial intelligence>Machine learning**

Sklep: Gigant.pl

### Statistical Learning for Biomedical Data Cambridge University Press

**Książki / Literatura obcojęzyczna**

This book is for anyone who has biomedical data and needs to identify variables that predict an outcome, for two-group outcomes such as tumor/not-tumor, survival/death, or response from treatment. Statistical learning machines are ideally suited to these types of prediction problems, especially if the variables being studied may not meet the assumptions of traditional techniques. Learning machines come from the world of probability and computer science but are not yet widely used in biomedical research. This introduction brings learning machine techniques to the biomedical world in an accessible way, explaining the underlying principles in nontechnical language and using extensive examples and figures. The authors connect these new methods to familiar techniques by showing how to use the learning machine models to generate smaller, more easily interpretable traditional models. Coverage includes single decision trees, multiple-tree techniques such as Random Forests

Sklep: Libristo.pl

### Introduction to Machine Learning MIT Press

**Książki / Literatura obcojęzyczna**

The goal of machine learning is to program computers to use example data or past experience to solve a given problem. Many successful applications of machine learning exist already, including systems that analyze past sales data to predict customer behavior, 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. Subjects include supervised learning; Bayesian decision theory; parametric, semi-parametric, and nonparametric methods; multivariate analysis; hidden Markov models; reinforcement learning; kernel machines; graphical models; Bayesian estimation; and statistical testing. Machine learning is rapidly becoming a skill that computer science students must master before graduation. The third edition of Introduction to Machine Learning reflects this shift, with added support for beginners, including selected solutions for exercises and additional example data sets (with code available online). Other substantial changes include discussions of outlier detection; ranking algorithms for perceptrons and support vector machines; matrix decomposition and spectral methods; distance estimation; new kernel algorithms; deep learning in multilayered perceptrons; and the nonparametric approach to Bayesian methods. All learning algorithms are explained so that students can easily move from the equations in the book to a computer program. The book can be used by both advanced undergraduates and graduate students. It will also be of interest to professionals who are concerned with the application of machine learning methods.

Sklep: Libristo.pl

### An Introduction To Support Vector Machines And Other Kernel - Based Learning Methods

**Książki Obcojęzyczne>Angielskie>Computing & information technology>Computer science>Artificial intelligence>Machine learningKsiążki Obc...**

A Comprehensive Introduction To This Recent Method For Machine Learning And Data Mining.

Sklep: Gigant.pl

### Machine Learning in Medicine - Cookbook Two Springer, Berlin

**Książki / Literatura obcojęzyczna**

The amount of data medical databases doubles every 20 months, and physicians are at a loss to analyze them. Also, traditional data analysis has difficulty to identify outliers and patterns in big data and data with multiple exposure / outcome variables and analysis-rules for surveys and questionnaires, currently common methods of data collection, are, essentially, missing. Consequently, proper data-based health decisions will soon be impossible.§§Obviously, it is time that medical and health professionals mastered their reluctance to use machine learning methods and this was the main incentive for the authors to complete a series of three textbooks entitled Machine Learning in Medicine Part One, Two and Three, Springer Heidelberg Germany, 2012-2013", describing in a nonmathematical way over sixty machine learning methodologies, as available in SPSS statistical software and other major software programs. Although well received, it came to our attention that physicians and students often lacked time to read the entire books, and requested a small book, without background information and theoretical discussions and highlighting technical details.§§For this reason we produced a 100 page cookbook, entitled "Machine Learning in Medicine - Cookbook One", with data examples available at extras.springer.com for self-assessment and with reference to the above textbooks for background information. Already at the completion of this cookbook we came to realize, that many essential methods were not covered. The current volume, entitled "Machine Learning in Medicine - Cookbook Two" is complementary to the first and also intended for providing a more balanced view of the field and thus, as a must-read not only for physicians and students, but also for any one involved in the process and progress of health and health care.§§Similarly to Machine Learning in Medicine - Cookbook One, the current work will describe stepwise analyses of over twenty machine learning methods, that are, likewise, based on the three major machine learning methodologies:§§Cluster methodologies (Chaps. 1-3)§§Linear methodologies (Chaps. 4-11)§§Rules methodologies (Chaps. 12-20) §In extras.springer.com the data files of the examples are given, as well as XML (Extended Mark up Language), SPS (Syntax) and ZIP (compressed) files for outcome predictions in future patients. In addition to condensed versions of the methods, fully described in the above three textbooks, an introduction is given to SPSS Modeler (SPSS' data mining workbench) in the Chaps. 15, 18, 19, while improved statistical methods like various automated analyses and Monte Carlo simulation models are in the Chaps. 1, 5, 7 and 8.§§We should emphasize that all of the methods described have been successfully applied in practice by the authors, both of them professors in applied statistics and machine learning at the European Community College of Pharmaceutical Medicine in Lyon France. We recommend the current work not only as a training companion to investigators and students, because of plenty of step by step analyses given, but also as a brief introductory text to jaded clinicians new to the methods. For the latter purpose, background and theoretical information have been replaced with the appropriate references to the above textbooks, while single sections addressing "general purposes", "main scientific questions" and "conclusions" are given in place.§§Finally, we will demonstrate that modern machine learning performs sometimes better than traditional statistics does. Machine learning may have little options for adjusting confounding and interaction, but you can add propensity scores and interaction variables to almost any machine learning method.§

Sklep: Libristo.pl

### Machine Learning Academic Press Inc

**Książki / Literatura obcojęzyczna**

This tutorial text gives a unifying perspective on machine learning by covering both probabilistic and deterministic approaches, which rely on optimization techniques, as well as Bayesian inference, which is based on a hierarchy of probabilistic models. The book presents the major machine learning methods as they have been developed in different disciplines, such as statistics, statistical and adaptive signal processing and computer science. Focusing on the physical reasoning behind the mathematics, all the various methods and techniques are explained in depth, supported by examples and problems, giving an invaluable resource to the student and researcher for understanding and applying machine learning concepts. The book builds carefully from the basic classical methods to the most recent trends, with chapters written to be as self-contained as possible, making the text suitable for different courses: pattern recognition, statistical/adaptive signal processing, statistical/Bayesian learning, as well as short courses on sparse modeling, deep learning, and probabilistic graphical models. * All major classical techniques: Mean/Least-Squares regression and filtering, Kalman filtering, stochastic approximation and online learning, Bayesian classification, decision trees, logistic regression and boosting methods.* The latest trends: Sparsity, convex analysis and optimization, online distributed algorithms, learning in RKH spaces, Bayesian inference, graphical and hidden Markov models, particle filtering, deep learning, dictionary learning and latent modeling.* Case studies - protein folding prediction, optical character recognition, text authorship identification, fMRI data analysis, change point detection, hyperspectral image unmixing, target localization, channel equalization and echo cancellation, show how the theory can be applied.* MATLAB code for all the main algorithms are available on an accompanying website, enabling the reader to experiment with the code.

Sklep: Libristo.pl

### Pattern Recognition and Machine Learning Springer

**Książki / Literatura obcojęzyczna**

The dramatic growth in practical applications for machine learning over the last ten years has been accompanied by many important developments in the underlying algorithms and techniques. For example, 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 techniques. The practical applicability of Bayesian methods has been greatly enhanced by the development of a range of approximate inference algorithms such as variational Bayes and expectation propagation, while new models based on kernels have had a significant impact on both algorithms and applications.This completely 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. No previous knowledge of pattern recognition or machine learning concepts is assumed. Familiarity with multivariate calculus and basic linear algebra isrequired, and some experience in the use of probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.The book is suitable for courses on machine learning, statistics, computer science, signal processing, computer vision, data mining, and bioinformatics. Extensive support is provided for course instructors, including more than 400 exercises, graded according to difficulty. Example solutions for a subset of the exercises are available from the book web site, while solutions for the remainder can be obtained by instructors from the publisher. The book is supported by a great deal of additional material, and the reader is encouraged to visit the book web site for the latest information.Coming soon: For students, worked solutions to a subset of exercises available on a public web site (for exercises marked "www" in the text) For instructors, worked solutions to remaining exercises from the Springer web site Lecture slides to accompany each chapter Data sets available for download

Sklep: Libristo.pl

### Categorization and Machine Learning Books on Demand

**Książki / Literatura obcojęzyczna**

Machine learning is the attempt to imitate human categorization of perceived reality in computers. It is driven by the desire to provide machines that are as open-minded, intelligent and flexible as humans. The central goal is to provide classifications for arbitrary types of input data: Labels that characterize the data correctly, given some examples. Machine learning has been a research topic of computer science for several decades. This book summarizes the major findings, explains the practically relevant methods and discusses their communalities and differences. In the first of three parts, we introduce the setting, goals and all necessary tools for the definition, application and evaluation of learning algorithms. The second part discusses and compares the various algorithms employed in machine categorization today. We structure them in four groups: the optimization algorithms, risk minimization approaches, those that employ probabilistic inference and those that imitate neural inference processes. Outstanding examples from the list of algorithms are the vector space mode, the support vector machine, Bayes and Markov processes, conditional random fields, radial basis function networks and methods employed for deep learning such as the Boltzmann machine. The third part reviews the algorithms and explores the theoretical frontiers of machine learning. In summary, we endeavor to provide a comprehensive yet intuitive introduction into the field of categorization. Neither parallels to human cognition are neglected nor recent developments in algorithm design or theoretical justification. As a research field, machine learning is gaining more and more attention. This book explains what it is, where it can be applied and how it is done.

Sklep: Libristo.pl

### Machine Learning Springer, Berlin

**Książki / Literatura obcojęzyczna**

Machine Learning - Modeling Data Locally and Globally presents a novel and unified theory that tries to seamlessly integrate different algorithms. Specifically, the book distinguishes the inner nature of machine learning algorithms as either "local learning"or "global learning."This theory not only connects previous machine learning methods, or serves as roadmap in various models, but more importantly it also motivates a theory that can learn from data both locally and globally. This would help the researchers gain a deeper insight and comprehensive understanding of the techniques in this field. The book reviews current topics,new theories and applications.§Kaizhu Huang was a researcher at the Fujitsu Research and Development Center and is currently a research fellow in the Chinese University of Hong Kong. Haiqin Yang leads the image processing group at HiSilicon Technologies. Irwin King and Michael R. Lyu are professors at the Computer Science and Engineering department of the Chinese University of Hong Kong.

Sklep: Libristo.pl

### Practical Data Science with R MANNING

**Książki / Literatura obcojęzyczna**

DESCRIPTION Simply put, data science is the discipline of extracting meaning from data. While it can involve deep knowledge of statistics, mathematics, machine learning, and computer science, for most non-academics, data science looks like applying analysis techniques to answer key business questions. Practical Data Science with R lives up to its name. It explains basic principles without the theoretical mumbo-jumbo and jumps right to the real use cases faced while collecting, curating, and analyzing the data crucial to the success of businesses. Readers will apply the R programming language and statistical analysis techniques to carefully-explained examples based in marketing, business intelligence, and decision support, while learning how to create instrumentation, design experiments such as A/B tests, and accurately present data to audiences of all levels. RETAIL SELLING POINTS Demonstrations of need-to-know statistical ideas Covers all aspects of the project lifecycle Data science for the motivated business professional AUDIENCE Written for the business analyst, technical consultant or technical director- no formal statistics or mathematics background is required. Readers should be comfortable with quantitative thinking plus light scripting or programming. Some familiarity with R is a plus. ABOUT THE TECHNOLOGY R is a programming language which is used for developing statistical software programs. Data Science is the process of collecting data and developing analysis techniques and software over that data to answer key business questions.

Sklep: Libristo.pl

### Advances in Independent Component Analysis and Learning Mach ACADEMIC PRESS

**Książki / Literatura obcojęzyczna**

In honour of Professor Erkki Oja, one of the pioneers of Independent Component Analysis (ICA), this book reviews key advances in the theory and application of ICA, as well as its influence on signal processing, pattern recognition, machine learning, and data mining. Examples of topics which have developed from the advances of ICA, which are covered in the book are: * A unifying probabilistic model for PCA and ICA * Optimization methods for matrix decompositions * Insights into the FastICA algorithm* Unsupervised deep learning * Machine vision and image retrieval * A review of developments in the theory and applications of independent component analysis, and its influence in important areas such as statistical signal processing, pattern recognition and deep learning.* A diverse set of application fields, ranging from machine vision to science policy data.* Contributions from leading researchers in the field.

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### COMPUTER AGE STATISTICAL INFERENCE CAMBRIDGE GENERAL ACADEMIC

**Książki / Literatura obcojęzyczna**

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.

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### Statistical Challenges in 21st Century Cosmology (IAU S306) Cambridge University Press

**Książki / Literatura obcojęzyczna**

The advent of advanced astronomical instruments and huge surveys means that the twenty-first century is witnessing a rapid growth in astrostatistical science. Interpreting the cosmic microwave background, weak and strong gravitational lensing, galaxy clustering and other signatures of the early Universe all require advanced statistical techniques. Led by members of the IAU's newly formed Working Group in Astrostatistics and Astroinformatics, IAU Symposium 306 emphasises the intricate mathematical methods needed to extract scientific insights from large and complicated datasets. It contains contributions on Bayesian methods, weak lensing cosmology, CMB data analysis, cross-correlating datasets, large-scale structure, data mining and machine learning, ongoing surveys and the future Euclid mission. The approaches presented here provide a solid foundation to advance new research methods in cosmology, making it an essential text for the large community of astronomers and statisticians who will analyse and interpret the vast and growing amount of observational data.

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### Frontiers of Statistical Decision Making and Bayesian Analysis Springer, Berlin

**Książki / Literatura obcojęzyczna**

Research in Bayesian analysis and statistical decision theory is rapidly expanding and diversifying, making it increasingly more difficult for any single researcher to stay up to date on all current research frontiers. This book provides a review of current research challenges and opportunities. While the book can not exhaustively cover all current research areas, it does include some exemplary discussion of most research frontiers. Topics include objective Bayesian inference, shrinkage estimation and other decision based estimation, model selection and testing, nonparametric Bayes, the interface of Bayesian and frequentist inference, data mining and machine learning, methods for categorical and spatio-temporal data analysis and posterior simulation methods. Several major application areas are covered: computer models, Bayesian clinical trial design, epidemiology, phylogenetics, bioinformatics, climate modeling and applications in political science, finance and marketing. As a review of current research in Bayesian analysis the book presents a balance between theory and applications. The lack of a clear demarcation between theoretical and applied research is a reflection of the highly interdisciplinary and often applied nature of research in Bayesian statistics. The book is intended as an update for researchers in Bayesian statistics, including non-statisticians who make use of Bayesian inference to address substantive research questions in other fields. It would also be useful for graduate students and research scholars in statistics or biostatistics who wish to acquaint themselves with current research frontiers.

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### Foundations of Data Mining and Knowledge Discovery Springer, Berlin

**Książki / Literatura obcojęzyczna**

"Foundations of Data Mining and Knowledge Discovery" contains the latest results and new directions in data mining research. Data mining, which integrates various technologies, including computational intelligence, database and knowledge management, machine learning, soft computing, and statistics, is one of the fastest growing fields in computer science. Although many data mining techniques have been developed, further development of the field requires a close examination of its foundations. This volume presents the results of investigations into the foundations of the discipline, and represents the state of the art for much of the current research. This book will prove extremely valuable and fruitful for data mining researchers, no matter whether they would like to uncover the fundamental principles behind data mining, or apply the theories to practical applications. TOC:From the contents: §Part I Foundations of Data Mining; Knowledge Discovery as Translation; Mathematical Foundation of Association Rules Mining Associations by Solving Integral Linear Inequalities; Comparative Study of Sequential Pattern Mining Models; Designing Robust Regression Models; A Probabilistic Logic-based Framework for Characterizing Knowledge Discovery in Databases; A Careful Look at the Use of Statistical Methodology in Data Mining; Justification and Hypothesis Selection in Data Mining.- Part II Methods of Data Mining; A Comparative Investigation on Model Selection in Binary Factor Analysis; Extraction of Generalized Rules with Automated Attribute Abstraction; Decision Making Based on Hybrid of Multi-knowledge and Na

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