Download foundations of machine learning adaptive computation and machine learning series in pdf or read foundations of machine learning adaptive computation and machine learning series in pdf online books in PDF, EPUB and Mobi Format. Click Download or Read Online button to get foundations of machine learning adaptive computation and machine learning series in pdf book now. This site is like a library, Use search box in the widget to get ebook that you want.



Foundations Of Machine Learning

Author: Mehryar Mohri
Publisher: MIT Press
ISBN: 026201825X
Size: 44.25 MB
Format: PDF, ePub, Mobi
View: 4193
Download and Read
Fundamental topics in machine learning are presented along with theoretical and conceptual tools for the discussion and proof of algorithms.

Big Data Technologies And Applications

Author: Borko Furht
Publisher: Springer
ISBN: 3319445502
Size: 63.11 MB
Format: PDF
View: 7616
Download and Read
The objective of this book is to introduce the basic concepts of big data computing and then to describe the total solution of big data problems using HPCC, an open-source computing platform. The book comprises 15 chapters broken into three parts. The first part, Big Data Technologies, includes introductions to big data concepts and techniques; big data analytics; and visualization and learning techniques. The second part, LexisNexis Risk Solution to Big Data, focuses on specific technologies and techniques developed at LexisNexis to solve critical problems that use big data analytics. It covers the open source High Performance Computing Cluster (HPCC Systems®) platform and its architecture, as well as parallel data languages ECL and KEL, developed to effectively solve big data problems. The third part, Big Data Applications, describes various data intensive applications solved on HPCC Systems. It includes applications such as cyber security, social network analytics including fraud, Ebola spread modeling using big data analytics, unsupervised learning, and image classification. The book is intended for a wide variety of people including researchers, scientists, programmers, engineers, designers, developers, educators, and students. This book can also be beneficial for business managers, entrepreneurs, and investors. /div

Modern Computational Models Of Semantic Discovery In Natural Language

Author: Žižka, Jan
Publisher: IGI Global
ISBN: 146668691X
Size: 42.26 MB
Format: PDF, Docs
View: 968
Download and Read
Language—that is, oral or written content that references abstract concepts in subtle ways—is what sets us apart as a species, and in an age defined by such content, language has become both the fuel and the currency of our modern information society. This has posed a vexing new challenge for linguists and engineers working in the field of language-processing: how do we parse and process not just language itself, but language in vast, overwhelming quantities? Modern Computational Models of Semantic Discovery in Natural Language compiles and reviews the most prominent linguistic theories into a single source that serves as an essential reference for future solutions to one of the most important challenges of our age. This comprehensive publication benefits an audience of students and professionals, researchers, and practitioners of linguistics and language discovery. This book includes a comprehensive range of topics and chapters covering digital media, social interaction in online environments, text and data mining, language processing and translation, and contextual documentation, among others.

Nostradamus 2014 Prediction Modeling And Analysis Of Complex Systems

Author: Ivan Zelinka
Publisher: Springer
ISBN: 3319074016
Size: 10.60 MB
Format: PDF
View: 6551
Download and Read
The prediction of behavior of complex systems, analysis and modeling of its structure is a vitally important problem in engineering, economy and generally in science today. Examples of such systems can be seen in the world around us (including our bodies) and of course in almost every scientific discipline including such “exotic” domains as the earth’s atmosphere, turbulent fluids, economics (exchange rate and stock markets), population growth, physics (control of plasma), information flow in social networks and its dynamics, chemistry and complex networks. To understand such complex dynamics, which often exhibit strange behavior, and to use it in research or industrial applications, it is paramount to create its models. For this purpose there exists a rich spectrum of methods, from classical such as ARMA models or Box Jenkins method to modern ones like evolutionary computation, neural networks, fuzzy logic, geometry, deterministic chaos amongst others. This proceedings book is a collection of accepted papers of the Nostradamus conference that has been held in Ostrava, Czech Republic in June 2014. This book also includes outstanding keynote lectures by distinguished guest speakers: René Lozi (France), Ponnuthurai Nagaratnam Suganthan (Singapore) and Lars Nolle (Germany). The main aim of the conference was to create a periodical possibility for students, academics and researchers to exchange their ideas and novel research methods. This conference establishes a forum for presentation and discussion of recent research trends in the area of applications of various predictive methods.

Reinforcement Learning

Author: Richard S. Sutton
Publisher: MIT Press
ISBN: 9780262193986
Size: 53.86 MB
Format: PDF, ePub
View: 3661
Download and Read
An account of key ideas and algorithms in reinforcement learning. The discussion ranges from the history of the field's intellectual foundations to recent developments and applications. Areas studied include reinforcement learning problems in terms of Markov decision problems and solution methods.

Boosting

Author: Robert E. Schapire
Publisher: MIT Press
ISBN: 0262017180
Size: 67.67 MB
Format: PDF, ePub, Mobi
View: 1714
Download and Read
Boosting is an approach to machine learning based on the idea of creating a highly accurate predictor by combining many weak and inaccurate "rules of thumb." A remarkably rich theory has evolved around boosting, with connections to a range of topics, including statistics, game theory, convex optimization, and information geometry. Boosting algorithms have also enjoyed practical success in such fields as biology, vision, and speech processing. At various times in its history, boosting has been perceived as mysterious, controversial, even paradoxical. This book, written by the inventors of the method, brings together, organizes, simplifies, and substantially extends two decades of research on boosting, presenting both theory and applications in a way that is accessible to readers from diverse backgrounds while also providing an authoritative reference for advanced researchers. With its introductory treatment of all material and its inclusion of exercises in every chapter, the book is appropriate for course use as well. The book begins with a general introduction to machine learning algorithms and their analysis; then explores the core theory of boosting, especially its ability to generalize; examines some of the myriad other theoretical viewpoints that help to explain and understand boosting; provides practical extensions of boosting for more complex learning problems; and finally presents a number of advanced theoretical topics. Numerous applications and practical illustrations are offered throughout.

Foundations Of Knowledge Acquisition

Author: Alan L. Meyrowitz
Publisher: Springer Science & Business Media
ISBN: 0585273669
Size: 67.71 MB
Format: PDF, ePub, Mobi
View: 7344
Download and Read
One of the most intriguing questions about the new computer technology that has appeared over the past few decades is whether we humans will ever be able to make computers learn. As is painfully obvious to even the most casual computer user, most current computers do not. Yet if we could devise learning techniques that enable computers to routinely improve their performance through experience, the impact would be enormous. The result would be an explosion of new computer applications that would suddenly become economically feasible (e. g. , personalized computer assistants that automatically tune themselves to the needs of individual users), and a dramatic improvement in the quality of current computer applications (e. g. , imagine an airline scheduling program that improves its scheduling method based on analyzing past delays). And while the potential economic impact of successful learning methods is sufficient reason to invest in research into machine learning, there is a second significant reason: studying machine learning helps us understand our own human learning abilities and disabilities, leading to the possibility of improved methods in education. While many open questions remain about the methods by which machines and humans might learn, significant progress has been made.

Mahout In Action

Author: Sean Owen
Publisher: Manning Publications
ISBN: 9781935182689
Size: 73.26 MB
Format: PDF
View: 118
Download and Read
Presents information on machine learning through the use of Apache Mahout, covering such topics as using group data to make individual recommendations, finding logical clusters, and filtering classifications.

Elements Of Causal Inference

Author: Jonas Peters
Publisher: MIT Press
ISBN: 0262037319
Size: 52.85 MB
Format: PDF, ePub
View: 7321
Download and Read
The mathematization of causality is a relatively recent development, and has become increasingly important in data science and machine learning. This book offers a self-contained and concise introduction to causal models and how to learn them from data. After explaining the need for causal models and discussing some of the principles underlying causal inference, the book teaches readers how to use causal models: how to compute intervention distributions, how to infer causal models from observational and interventional data, and how causal ideas could be exploited for classical machine learning problems. All of these topics are discussed first in terms of two variables and then in the more general multivariate case. The bivariate case turns out to be a particularly hard problem for causal learning because there are no conditional independences as used by classical methods for solving multivariate cases. The authors consider analyzing statistical asymmetries between cause and effect to be highly instructive, and they report on their decade of intensive research into this problem. The book is accessible to readers with a background in machine learning or statistics, and can be used in graduate courses or as a reference for researchers. The text includes code snippets that can be copied and pasted, exercises, and an appendix with a summary of the most important technical concepts.

Mastering Java Machine Learning

Author: Dr. Uday Kamath
Publisher: Packt Publishing Ltd
ISBN: 1785888552
Size: 32.93 MB
Format: PDF, Kindle
View: 2280
Download and Read
Become an advanced practitioner with this progressive set of master classes on application-oriented machine learning About This Book Comprehensive coverage of key topics in machine learning with an emphasis on both the theoretical and practical aspects More than 15 open source Java tools in a wide range of techniques, with code and practical usage. More than 10 real-world case studies in machine learning highlighting techniques ranging from data ingestion up to analyzing the results of experiments, all preparing the user for the practical, real-world use of tools and data analysis. Who This Book Is For This book will appeal to anyone with a serious interest in topics in Data Science or those already working in related areas: ideally, intermediate-level data analysts and data scientists with experience in Java. Preferably, you will have experience with the fundamentals of machine learning and now have a desire to explore the area further, are up to grappling with the mathematical complexities of its algorithms, and you wish to learn the complete ins and outs of practical machine learning. What You Will Learn Master key Java machine learning libraries, and what kind of problem each can solve, with theory and practical guidance. Explore powerful techniques in each major category of machine learning such as classification, clustering, anomaly detection, graph modeling, and text mining. Apply machine learning to real-world data with methodologies, processes, applications, and analysis. Techniques and experiments developed around the latest specializations in machine learning, such as deep learning, stream data mining, and active and semi-supervised learning. Build high-performing, real-time, adaptive predictive models for batch- and stream-based big data learning using the latest tools and methodologies. Get a deeper understanding of technologies leading towards a more powerful AI applicable in various domains such as Security, Financial Crime, Internet of Things, social networking, and so on. In Detail Java is one of the main languages used by practicing data scientists; much of the Hadoop ecosystem is Java-based, and it is certainly the language that most production systems in Data Science are written in. If you know Java, Mastering Machine Learning with Java is your next step on the path to becoming an advanced practitioner in Data Science. This book aims to introduce you to an array of advanced techniques in machine learning, including classification, clustering, anomaly detection, stream learning, active learning, semi-supervised learning, probabilistic graph modeling, text mining, deep learning, and big data batch and stream machine learning. Accompanying each chapter are illustrative examples and real-world case studies that show how to apply the newly learned techniques using sound methodologies and the best Java-based tools available today. On completing this book, you will have an understanding of the tools and techniques for building powerful machine learning models to solve data science problems in just about any domain. Style and approach A practical guide to help you explore machine learning—and an array of Java-based tools and frameworks—with the help of practical examples and real-world use cases.