Introduction to Statistical Machine Learning Elsevier Books

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Machine learning allows computers to learn and discern patterns without actually being programmed. When Statistical techniques and machine learning are combined together they are a powerful tool for analysing various kinds of data in many computer science/engineering areas including, image processing, speech processing, natural language processing, robot control, as well as in fundamental sciences such as biology, medicine, astronomy, physics, and materials. Introduction to Statistical Machine Learning provides a general introduction to machine learning that covers a wide range of topics concisely and will help you bridge the gap between theory and practice. Part I discusses the fundamental concepts of statistics and probability that are used in describing machine learning algorithms. Part II and Part III explain the two major approaches of machine learning techniques; generative methods and discriminative methods. While Part III provides an in-depth look at advanced topics that play essential roles in making machine learning algorithms more useful in practice. The accompanying MATLAB/Octave programs provide you with the necessary practical skills needed to accomplish a wide range of data analysis tasks. Provides the necessary background material to understand machine learning such as statistics, probability, linear algebra, and calculus.Complete coverage of the generative approach to statistical pattern recognition and the discriminative approach to statistical machine learning.Includes MATLAB/Octave programs so that readers can test the algorithms numerically and acquire both mathematical and practical skills in a wide range of data analysis tasksDiscusses a wide range of applications in machine learning and statistics and provides examples drawn from image processing, speech processing, natural language processing, robot control, as well as biology, medicine, astronomy, physics, and materials.

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Introduction To Statistical Machine Learning

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

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Introduction To Statistical Machine Learning

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

Sklep: Gigant.pl

Introduction to Machine Learning MIT Press Ltd

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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.

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Machine Learning Using R APress

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This book is inspired by the Machine Learning Model Building Process Flow, which provides the reader the ability to understand a ML algorithm and apply the entire process of building a ML model from the raw data.This new paradigm of teaching Machine Learning will bring about a radical change in perception for many of those who think this subject is difficult to learn. Though theory sometimes looks difficult, especially when there is heavy mathematics involved, the seamless flow from the theoretical aspects to example-driven learning provided in Blockchain and Capitalism makes it easy for someone to connect the dots.For every Machine Learning algorithm covered in this book, a 3-D approach of theory, case-study and practice will be given. And where appropriate, the mathematics will be explained through visualization in R. All practical demonstrations will be explored in R, a powerful programming language and software environment for statistical computing and graphics. The various packages and methods available in R will be used to explain the topics. In the end, readers will learn some of the latest technological advancements in building a scalable machine learning model with Big Data. Who This Book is For: Data scientists, data science professionals and researchers in academia who want to understand the nuances of Machine learning approaches/algorithms along with ways to see them in practice using R. The book will also benefit the readers who want to understand the technology behind implementing a scalable machine learning model using Apache Hadoop, Hive, Pig and Spark. What you will learn: 1. ML model building process flow2. Theoretical aspects of Machine Learning3. Industry based Case-Study4. Example based understanding of ML algorithm using R5. Building ML models using Apache Hadoop and Spark

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Machine Learning with R Springer Verlag, Singapore

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This book helps readers understand the mathematics of machine learning, and apply them in different situations. It is divided into two basic parts, the first of which introduces readers to the theory of linear algebra, probability, and data distributions and it's applications to machine learning. It also includes a detailed introduction to the concepts and constraints of machine learning and what is involved in designing a learning algorithm. This part helps readers understand the mathematical and statistical aspects of machine learning. In turn, the second part discusses the algorithms used in supervised and unsupervised learning. It works out each learning algorithm mathematically and encodes it in R to produce customized learning applications. In the process, it touches upon the specifics of each algorithm and the science behind its formulation. The book includes a wealth of worked-out examples along with R codes. It explains the code for each algorithm, and readers can modify the code to suit their own needs. The book will be of interest to all researchers who intend to use R for machine learning, and those who are interested in the practical aspects of implementing learning algorithms for data analysis. Further, it will be particularly useful and informative for anyone who has struggled to relate the concepts of mathematics and statistics to machine learning.

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Statistics, Data Mining, and Machine Learning in Astronomy Princeton University Press

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As telescopes, detectors, and computers grow ever more powerful, the volume of data at the disposal of astronomers and astrophysicists will enter the petabyte domain, providing accurate measurements for billions of celestial objects. This book provides a comprehensive and accessible introduction to the cutting-edge statistical methods needed to efficiently analyze complex data sets from astronomical surveys such as the Panoramic Survey Telescope and Rapid Response System, the Dark Energy Survey, and the upcoming Large Synoptic Survey Telescope. It serves as a practical handbook for graduate students and advanced undergraduates in physics and astronomy, and as an indispensable reference for researchers. Statistics, Data Mining, and Machine Learning in Astronomy presents a wealth of practical analysis problems, evaluates techniques for solving them, and explains how to use various approaches for different types and sizes of data sets. For all applications described in the book, Python code and example data sets are provided. The supporting data sets have been carefully selected from contemporary astronomical surveys (for example, the Sloan Digital Sky Survey) and are easy to download and use. The accompanying Python code is publicly available, well documented, and follows uniform coding standards. Together, the data sets and code enable readers to reproduce all the figures and examples, evaluate the methods, and adapt them to their own fields of interest. It describes the most useful statistical and data-mining methods for extracting knowledge from huge and complex astronomical data sets. It features real-world data sets from contemporary astronomical surveys. It uses a freely available Python codebase throughout Ideal for students and working astronomers.

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Introduction to Deep Learning Using R APress

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Understand deep learning, the nuances of its different models, and where these models can be applied.The abundance of data and demand for superior products/services have driven the development of advanced computer science techniques, among them image and speech recognition. Introduction to Deep Learning Using R provides a theoretical and practical understanding of the models that perform these tasks by building upon the fundamentals of data science through machine learning and deep learning. This step-by-step guide will help you understand the disciplines so that you can apply the methodology in a variety of contexts. All examples are taught in the R statistical language, allowing students and professionals to implement these techniques using open source tools.What You'll LearnUnderstand the intuition and mathematics that power deep learning models Utilize various algorithms using the R programming language and its packages Use best practices for experimental design and variable selection Practice the methodology to approach and effectively solve problems as a data scientist Evaluate the effectiveness of algorithmic solutions and enhance their predictive power Who This Book Is For Students, researchers, and data scientists who are familiar with programming using R. This book also is also of use for those who wish to learn how to appropriately deploy these algorithms in applications where they would be most useful.

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Targeted Learning in Data Science Springer International Publishing AG

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This textbook for Masters and PhD graduate students in biostatistics, statistics, data science, and epidemiology deals with the practical challenges that come with big, complex, and dynamic data while maintaining a strong theoretical foundation. It presents a scientific roadmap to translate real-world data science applications into formal statistical estimation problems by using the general template of targeted maximum likelihood estimators to construct targeted machine learning algorithms that incorporate state of the art applications to estimate quantities of interest, while still providing valid inference. Targeted learning methods within data science are a critical component for answering complex statistical questions in the modern age. The techniques can answer complex questions including optimal rules for assigning treatment based on longitudinal data involving time-dependent confounding and censoring as well as other estimands in dependent data structures, such as networks. Standard methods and software tools are not currently equipped for these challenges; however, targeted learning is tailored for these problems found in precision medicine, big data, and data science. Included in Targeted Learning in Data Science are demonstrations with software packages and real data sets that present a case that targeted learning is crucial for the next generation of statisticians and data scientists.

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Machine Learning Elsevier Books

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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.

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Pattern Recognition and Machine Learning Springer-Verlag New York Inc.

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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

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Categorization and Machine Learning Books on Demand

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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.

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Beginning Data Science in R APress

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Discover best practices for data analysis and software development in R and start on the path to becoming a fully-fledged data scientist. This book teaches you techniques for both data manipulation and visualization and shows you the best way for developing new software packages for R. Data Science in R details how data science is a combination of statistics, computational science, and machine learning. You'll see how to efficiently structure and mine data to extract useful patterns and build mathematical models. This requires computational methods and programming, and R is an ideal programming language for this. This book is based on a number of lecture notes for classes the author has taught on data science and statistical programming using the R programming language. Modern data analysis requires computational skills and usually a minimum of programming. What You Will LearnPerform data science and analytics using statistics and the R programming language Visualize and explore data, including working with large data sets found in big data Build an R package Test and check your code Practice version control Profile and optimize your code Who This Book Is For Those with some data science or analytics background, but not necessarily experience with the R programming language.

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Practical Data Science with R Manning Publications

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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.

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Mathematical Problems in Data Science Springer, Berlin

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This book describes current problems in data science and Big Data. Key topics are data classification, Graph Cut, the Laplacian Matrix, Google Page Rank, efficient algorithms, hardness of problems, different types of big data, geometric data structures, topological data processing, and various learning methods. For unsolved problems such as incomplete data relation and reconstruction, the book includes possible solutions and both statistical and computational methods for data analysis. Initial chapters focus on exploring the properties of incomplete data sets and partial-connectedness among data points or data sets. Discussions also cover the completion problem of Netflix matrix; machine learning method on massive data sets; image segmentation and video search. This book introduces software tools for data science and Big Data such MapReduce, Hadoop, and Spark.§§This book contains three parts. The first part explores the fundamental tools of data science. It includes basic graph theoretical methods, statistical and AI methods for massive data sets. In second part, chapters focus on the procedural treatment of data science problems including machine learning methods, mathematical image and video processing, topological data analysis, and statistical methods. The final section provides case studies on special topics in variational learning, mani§fold learning, business and financial data recovery, geometric search, and computing models.§§Mathematical Problems in Data Science is a valuable resource for researchers and professionals working in data science, information systems and networks. Advanced-level students studying computer science, electrical engineering and mathematics will also find the content helpful.§

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