AN INTRODUCTION TO MACHINE LEARNING THAT INCLUDES THE FUNDAMENTAL TECHNIQUES, METHODS, AND APPLICATIONS Machine Learning: a Concise Introduction offers a comprehensive introduction to the core concepts, approaches, and applications of machine learning. The author—an expert in the field—presents fundamental ideas, terminology, and techniques for solving applied problems in classification, regression, clustering, density estimation, and dimension reduction. The design principles behind the techniques are emphasized, including the bias-variance trade-off and its influence on the design of ensemble methods. Understanding these principles leads to more flexible and successful applications. Machine Learning: a Concise Introduction also includes methods for optimization, risk estimation, and model selection— essential elements of most applied projects. This important resource:
- Illustrates many classification methods with a single, running example, highlighting similarities and differences between methods
- Presents R source code which shows how to apply and interpret many of the techniques covered
- Includes many thoughtful exercises as an integral part of the text, with an appendix of selected solutions
- Contains useful information for effectively communicating with clients
Published by: Wiley | Publication date: 03/15/2018Kindle book details: Kindle Edition, 352 pages
Machine learning teaches computers to do what comes naturally to humans: learn from experience. Machine learning algorithms use computational methods to "learn" information directly from data without relying on a predetermined equation as a model. The algorithms adaptively improve their performance as the number of samples available for learning increases. Machine learning uses two types of techniques: supervised learning, which trains a model on known input and output data so that it can predict future outputs, and unsupervised learning, which finds hidden patterns or intrinsic structures in input data. This book develops supervised learning techniques for clustering (hierarchical clustering, non hierarchical clustering, Gaussian Mixture Models, Hidden Markov Models, Nearest Neighbors. KNN Classifiers, cluster visualization, Clusters with Self Organizing Map, Competitive Neural Networks, Competitive Layers, Autoencoders and clustering whit Neural Networks).
Publication date: 04/11/2017Kindle book details: Kindle Edition, 376 pages
The purpose of this handbook is to provide comprehensive tables of percentage points of the inverse Gaussian distribution. There is no other publication available today which condenses these tables - to such extent-in a concise, straightforward manner. The inverse Gaussian distribution is not only important for determining boundary crossing probabilities of Brownian Motion, which probabilities determine the operating characteristics of many sequential sampling procedures in statistics. It is also used in quality control procedures. This one-of-a-kind work includes a brief introductory section which outlines the inverse Gaussian distribution and explains the tables. The tables are produced in a fine grid of cumulative probabilities, and uses the closed form expression for the cumulative distribution function. This easy-to-use table reference also includes an excellent discussion of searching ordered tables. This handbook is a helpful, indispensable guide for all who are involved with statistics, mathematics, and computers. Mechanical engineers and physicists will find it useful also.
Published by: CRC Press | Publication date: 01/18/2018Kindle book details: Kindle Edition, 306 pages
Stochastic Analysis of Mixed Fractional Gaussian Processes presents the main tools necessary to characterize Gaussian processes. The book focuses on the particular case of the linear combination of independent fractional and sub-fractional Brownian motions with different Hurst indices. Stochastic integration with respect to these processes is considered, as is the study of the existence and uniqueness of solutions of related SDE's. Applications in finance and statistics are also explored, with each chapter supplying a number of exercises to illustrate key concepts.
- Presents both mixed fractional and sub-fractional Brownian motions
- Provides an accessible description for mixed fractional gaussian processes that is ideal for Master's and PhD students
- Includes different Hurst indices
Published by: ISTE Press - Elsevier | Publication date: 05/26/2018Kindle book details: Kindle Edition, 201 pages
Multilevel and Longitudinal Modeling Using Stata, Third Edition, by Sophia Rabe-Hesketh and Anders Skrondal, looks specifically at Stata’s treatment of generalized linear mixed models, also known as multilevel or hierarchical models. These models are “mixed” because they allow fixed and random effects, and they are “generalized” because they are appropriate for continuous Gaussian responses as well as binary, count, and other types of limited dependent variables.Volume I is devoted to continuous Gaussian linear mixed models and has nine chapters organized into four parts. The first part reviews the methods of linear regression. The second part provides in-depth coverage of two-level models, the simplest extensions of a linear regression model.
Published by: Stata Press | Publication date: 04/02/2012Kindle book details: Kindle Edition, 514 pages
Traditional books on machine learning can be divided into two groups — those aimed at advanced undergraduates or early postgraduates with reasonable mathematical knowledge and those that are primers on how to code algorithms. The field is ready for a text that not only demonstrates how to use the algorithms that make up machine learning methods, but also provides the background needed to understand how and why these algorithms work. Machine Learning: An Algorithmic Perspective is that text.Theory Backed up by Practical ExamplesThe book covers neural networks, graphical models, reinforcement learning, evolutionary algorithms, dimensionality reduction methods, and the important area of optimization. It treads the fine line between adequate academic rigor and overwhelming students with equations and mathematical concepts. The author addresses the topics in a practical way while providing complete information and references where other expositions can be found. He includes examples based on widely available datasets and practical and theoretical problems to test understanding and application of the material. The book describes algorithms with code examples backed up by a website that provides working implementations in Python. The author uses data from a variety of applications to demonstrate the methods and includes practical problems for students to solve.Highlights a Range of Disciplines and ApplicationsDrawing from computer science, statistics, mathematics, and engineering, the multidisciplinary nature of machine learning is underscored by its applicability to areas ranging from finance to biology and medicine to physics and chemistry. Written in an easily accessible style, this book bridges the gaps between disciplines, providing the ideal blend of theory and practical, applicable knowledge.
Published by: Chapman and Hall/CRC | Publication date: 03/23/2011Kindle book details: Kindle Edition, 406 pages
Filling the void between surveys of the field with relatively light mathematical content and books with a rigorous, formal approach to stochastic integration and probabilistic ideas, Stochastic Financial Models provides a sound introduction to mathematical finance. The author takes a classical applied mathematical approach, focusing on calculations rather than seeking the greatest generality.Developed from the esteemed author’s advanced undergraduate and graduate courses at the University of Cambridge, the text begins with the classical topics of utility and the mean-variance approach to portfolio choice. The remainder of the book deals with derivative pricing. The author fully explains the binomial model since it is central to understanding the pricing of derivatives by self-financing hedging portfolios. He then discusses the general discrete-time model, Brownian motion and the Black–Scholes model. The book concludes with a look at various interest-rate models. Concepts from measure-theoretic probability and solutions to the end-of-chapter exercises are provided in the appendices.By exploring the important and exciting application area of mathematical finance, this text encourages students to learn more about probability, martingales and stochastic integration. It shows how mathematical concepts, such as the Black–Scholes and Gaussian random-field models, are used in financial situations.
Published by: Chapman and Hall/CRC | Publication date: 04/19/2016Kindle book details: Kindle Edition, 264 pages
The author investigates athermal fluctuation from the viewpoints of statistical mechanics in this thesis. Stochastic methods are theoretically very powerful in describing fluctuation of thermodynamic quantities in small systems on the level of a single trajectory and have been recently developed on the basis of stochastic thermodynamics. This thesis proposes, for the first time, a systematic framework to describe athermal fluctuation, developing stochastic thermodynamics for non-Gaussian processes, while thermal fluctuations are mainly addressed from the viewpoint of Gaussian stochastic processes in most of the conventional studies. First, the book provides an elementary introduction to the stochastic processes and stochastic thermodynamics. The author derives a Langevin-like equation with non-Gaussian noise as a minimal stochastic model for athermal systems, and its analytical solution by developing systematic expansions is shown as the main result. Furthermore, the author shows a thermodynamic framework for such non-Gaussian fluctuations, and studies some thermodynamics phenomena, i.e. heat conduction and energy pumping, which shows distinct characteristics from conventional thermodynamics. The theory introduced in the book would be a systematic foundation to describe dynamics of athermal fluctuation quantitatively and to analyze their thermodynamic properties on the basis of stochastic methods.
Published by: Springer | Publication date: 11/20/2017Kindle book details: Kindle Edition, 222 pages
Stochastic Analysis for Gaussian Random Processes and Fields: With Applications (Chapman & Hall/CRC Monographs on Statistics & Applied Probability Book 145)
Stochastic Analysis for Gaussian Random Processes and Fields: With Applications presents Hilbert space methods to study deep analytic properties connecting probabilistic notions. In particular, it studies Gaussian random fields using reproducing kernel Hilbert spaces (RKHSs).The book begins with preliminary results on covariance and associated RKHS before introducing the Gaussian process and Gaussian random fields. The authors use chaos expansion to define the Skorokhod integral, which generalizes the Itô integral. They show how the Skorokhod integral is a dual operator of Skorokhod differentiation and the divergence operator of Malliavin. The authors also present Gaussian processes indexed by real numbers and obtain a Kallianpur–Striebel Bayes' formula for the filtering problem. After discussing the problem of equivalence and singularity of Gaussian random fields (including a generalization of the Girsanov theorem), the book concludes with the Markov property of Gaussian random fields indexed by measures and generalized Gaussian random fields indexed by Schwartz space. The Markov property for generalized random fields is connected to the Markov process generated by a Dirichlet form.
Published by: Chapman and Hall/CRC | Publication date: 06/23/2015Kindle book details: Kindle Edition, 201 pages
McGraw-Hill's 500 College Precalculus Questions: Ace Your College Exams: 3 Reading Tests + 3 Writing Tests + 3 Mathematics Tests (Mcgraw-hill's 500 Questions)
Sharpen your skills and prepare for your precalculus exam with a wealth of essential facts in a quick-and-easy Q&A format! Get the question-and-answer practice you need with McGraw-Hill's 500 College Precalculus Questions. Organized for easy reference and intensive practice, the questions cover all essential precalculus topics and include detailed answer explanations.The 500 practice questions are similar to course exam questions so you will know what to expect on test day. Each question includes a fully detailed answer that puts the subject in context. This additional practice helps you build your knowledge, strengthen test-taking skills, and build confidence. From ethical theory to epistemology, this book covers the key topics in precalculus. Prepare for exam day with:
- 500 essential precalculus questions and answers organized by subject
- Detailed answers that provide important context for studying
- Content that follows the current college 101 course curriculum
Published by: McGraw-Hill Education | Publication date: 12/21/2012Kindle book details: Kindle Edition, 176 pages