Large-Scale Machine Learning in the Earth Sciences

Author: Ashok N. Srivastava,Ramakrishna Nemani,Karsten Steinhaeuser

Publisher: CRC Press

ISBN: 1315354462

Category: Computers

Page: 208

View: 7482

From the Foreword: "While large-scale machine learning and data mining have greatly impacted a range of commercial applications, their use in the field of Earth sciences is still in the early stages. This book, edited by Ashok Srivastava, Ramakrishna Nemani, and Karsten Steinhaeuser, serves as an outstanding resource for anyone interested in the opportunities and challenges for the machine learning community in analyzing these data sets to answer questions of urgent societal interest...I hope that this book will inspire more computer scientists to focus on environmental applications, and Earth scientists to seek collaborations with researchers in machine learning and data mining to advance the frontiers in Earth sciences." --Vipin Kumar, University of Minnesota Large-Scale Machine Learning in the Earth Sciences provides researchers and practitioners with a broad overview of some of the key challenges in the intersection of Earth science, computer science, statistics, and related fields. It explores a wide range of topics and provides a compilation of recent research in the application of machine learning in the field of Earth Science. Making predictions based on observational data is a theme of the book, and the book includes chapters on the use of network science to understand and discover teleconnections in extreme climate and weather events, as well as using structured estimation in high dimensions. The use of ensemble machine learning models to combine predictions of global climate models using information from spatial and temporal patterns is also explored. The second part of the book features a discussion on statistical downscaling in climate with state-of-the-art scalable machine learning, as well as an overview of methods to understand and predict the proliferation of biological species due to changes in environmental conditions. The problem of using large-scale machine learning to study the formation of tornadoes is also explored in depth. The last part of the book covers the use of deep learning algorithms to classify images that have very high resolution, as well as the unmixing of spectral signals in remote sensing images of land cover. The authors also apply long-tail distributions to geoscience resources, in the final chapter of the book.

Data Science and Analytics with Python

Author: Jesus Rogel-Salazar

Publisher: CRC Press

ISBN: 1351647717

Category: Computers

Page: 400

View: 8957

Data Science and Analytics with Python is designed for practitioners in data science and data analytics in both academic and business environments. The aim is to present the reader with the main concepts used in data science using tools developed in Python, such as SciKit-learn, Pandas, Numpy, and others. The use of Python is of particular interest, given its recent popularity in the data science community. The book can be used by seasoned programmers and newcomers alike. The book is organized in a way that individual chapters are sufficiently independent from each other so that the reader is comfortable using the contents as a reference. The book discusses what data science and analytics are, from the point of view of the process and results obtained. Important features of Python are also covered, including a Python primer. The basic elements of machine learning, pattern recognition, and artificial intelligence that underpin the algorithms and implementations used in the rest of the book also appear in the first part of the book. Regression analysis using Python, clustering techniques, and classification algorithms are covered in the second part of the book. Hierarchical clustering, decision trees, and ensemble techniques are also explored, along with dimensionality reduction techniques and recommendation systems. The support vector machine algorithm and the Kernel trick are discussed in the last part of the book. About the Author Dr. Jesús Rogel-Salazar is a Lead Data scientist with experience in the field working for companies such as AKQA, IBM Data Science Studio, Dow Jones and others. He is a visiting researcher at the Department of Physics at Imperial College London, UK and a member of the School of Physics, Astronomy and Mathematics at the University of Hertfordshire, UK, He obtained his doctorate in physics at Imperial College London for work on quantum atom optics and ultra-cold matter. He has held a position as senior lecturer in mathematics as well as a consultant in the financial industry since 2006. He is the author of the book Essential Matlab and Octave, also published by CRC Press. His interests include mathematical modelling, data science, and optimization in a wide range of applications including optics, quantum mechanics, data journalism, and finance.

Earth Observation Open Science and Innovation

Author: Pierre-Philippe Mathieu,Christoph Aubrecht

Publisher: Springer

ISBN: 3319656333

Category: Science

Page: 330

View: 2039

This book is published open access under a CC BY 4.0 license. Over the past decades, rapid developments in digital and sensing technologies, such as the Cloud, Web and Internet of Things, have dramatically changed the way we live and work. The digital transformation is revolutionizing our ability to monitor our planet and transforming the way we access, process and exploit Earth Observation data from satellites. This book reviews these megatrends and their implications for the Earth Observation community as well as the wider data economy. It provides insight into new paradigms of Open Science and Innovation applied to space data, which are characterized by openness, access to large volume of complex data, wide availability of new community tools, new techniques for big data analytics such as Artificial Intelligence, unprecedented level of computing power, and new types of collaboration among researchers, innovators, entrepreneurs and citizen scientists. In addition, this book aims to provide readers with some reflections on the future of Earth Observation, highlighting through a series of use cases not just the new opportunities created by the New Space revolution, but also the new challenges that must be addressed in order to make the most of the large volume of complex and diverse data delivered by the new generation of satellites.

An Introduction to Statistical Learning

with Applications in R

Author: Gareth James,Daniela Witten,Trevor Hastie,Robert Tibshirani

Publisher: Springer Science & Business Media

ISBN: 1461471389

Category: Mathematics

Page: 426

View: 8929

An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform. Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.

Computer Age Statistical Inference

Algorithms, Evidence, and Data Science

Author: Bradley Efron,Trevor Hastie

Publisher: Cambridge University Press

ISBN: 1108107958

Category: Mathematics

Page: N.A

View: 6960

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.

Machine Learning in Action

Author: Peter Harrington

Publisher: Manning Publications

ISBN: 9781617290183

Category: Computers

Page: 354

View: 4031

Provides information on the concepts of machine theory, covering such topics as statistical data processing, data visualization, and forecasting.

Feature Engineering for Machine Learning and Data Analytics

Author: Guozhu Dong,Huan Liu

Publisher: CRC Press

ISBN: 1351721275

Category: Business & Economics

Page: 400

View: 843

Feature engineering plays a vital role in big data analytics. Machine learning and data mining algorithms cannot work without data. Little can be achieved if there are few features to represent the underlying data objects, and the quality of results of those algorithms largely depends on the quality of the available features. Feature Engineering for Machine Learning and Data Analytics provides a comprehensive introduction to feature engineering, including feature generation, feature extraction, feature transformation, feature selection, and feature analysis and evaluation. The book presents key concepts, methods, examples, and applications, as well as chapters on feature engineering for major data types such as texts, images, sequences, time series, graphs, streaming data, software engineering data, Twitter data, and social media data. It also contains generic feature generation approaches, as well as methods for generating tried-and-tested, hand-crafted, domain-specific features. The first chapter defines the concepts of features and feature engineering, offers an overview of the book, and provides pointers to topics not covered in this book. The next six chapters are devoted to feature engineering, including feature generation for specific data types. The subsequent four chapters cover generic approaches for feature engineering, namely feature selection, feature transformation based feature engineering, deep learning based feature engineering, and pattern based feature generation and engineering. The last three chapters discuss feature engineering for social bot detection, software management, and Twitter-based applications respectively. This book can be used as a reference for data analysts, big data scientists, data preprocessing workers, project managers, project developers, prediction modelers, professors, researchers, graduate students, and upper level undergraduate students. It can also be used as the primary text for courses on feature engineering, or as a supplement for courses on machine learning, data mining, and big data analytics.

Data Science and Analytics with Python

Author: Jesus Rogel-Salazar

Publisher: CRC Press

ISBN: 1351647717

Category: Computers

Page: 400

View: 8084

Data Science and Analytics with Python is designed for practitioners in data science and data analytics in both academic and business environments. The aim is to present the reader with the main concepts used in data science using tools developed in Python, such as SciKit-learn, Pandas, Numpy, and others. The use of Python is of particular interest, given its recent popularity in the data science community. The book can be used by seasoned programmers and newcomers alike. The book is organized in a way that individual chapters are sufficiently independent from each other so that the reader is comfortable using the contents as a reference. The book discusses what data science and analytics are, from the point of view of the process and results obtained. Important features of Python are also covered, including a Python primer. The basic elements of machine learning, pattern recognition, and artificial intelligence that underpin the algorithms and implementations used in the rest of the book also appear in the first part of the book. Regression analysis using Python, clustering techniques, and classification algorithms are covered in the second part of the book. Hierarchical clustering, decision trees, and ensemble techniques are also explored, along with dimensionality reduction techniques and recommendation systems. The support vector machine algorithm and the Kernel trick are discussed in the last part of the book. About the Author Dr. Jesús Rogel-Salazar is a Lead Data scientist with experience in the field working for companies such as AKQA, IBM Data Science Studio, Dow Jones and others. He is a visiting researcher at the Department of Physics at Imperial College London, UK and a member of the School of Physics, Astronomy and Mathematics at the University of Hertfordshire, UK, He obtained his doctorate in physics at Imperial College London for work on quantum atom optics and ultra-cold matter. He has held a position as senior lecturer in mathematics as well as a consultant in the financial industry since 2006. He is the author of the book Essential Matlab and Octave, also published by CRC Press. His interests include mathematical modelling, data science, and optimization in a wide range of applications including optics, quantum mechanics, data journalism, and finance.

Cloud Computing in Ocean and Atmospheric Sciences

Author: Tiffany C Vance,Nazila Merati,Chaowei Yang,May Yuan

Publisher: Elsevier

ISBN: 012803193X

Category: Science

Page: 454

View: 8296

Cloud Computing in Ocean and Atmospheric Sciences provides the latest information on this relatively new platform for scientific computing, which has great possibilities and challenges, including pricing and deployments costs and applications that are often presented as primarily business oriented. In addition, scientific users may be very familiar with these types of models and applications, but relatively unfamiliar with the intricacies of the hardware platforms they use. The book provides a range of practical examples of cloud applications that are written to be accessible to practitioners, researchers, and students in affiliated fields. By providing general information on the use of the cloud for oceanographic and atmospheric computing, as well as examples of specific applications, this book encourages and educates potential users of the cloud. The chapters provide an introduction to the practical aspects of deploying in the cloud, also providing examples of workflows and techniques that can be reused in new projects. Provides real examples that help new users quickly understand the cloud and provide guidance for new projects Presents proof of the usability of the techniques and a clear path to adoption of the techniques by other researchers Includes real research and development examples that are ideal for cloud computing adopters in ocean and atmospheric domains

Information Theory, Inference and Learning Algorithms

Author: David J. C. MacKay

Publisher: Cambridge University Press

ISBN: 9780521642989

Category: Computers

Page: 628

View: 7310

Fun and exciting textbook on the mathematics underpinning the most dynamic areas of modern science and engineering.

Big Data

Principles and Paradigms

Author: Rajkumar Buyya,Rodrigo N. Calheiros,Amir Vahid Dastjerdi

Publisher: Morgan Kaufmann

ISBN: 0128093463

Category: Computers

Page: 494

View: 2241

Big Data: Principles and Paradigms captures the state-of-the-art research on the architectural aspects, technologies, and applications of Big Data. The book identifies potential future directions and technologies that facilitate insight into numerous scientific, business, and consumer applications. To help realize Big Data’s full potential, the book addresses numerous challenges, offering the conceptual and technological solutions for tackling them. These challenges include life-cycle data management, large-scale storage, flexible processing infrastructure, data modeling, scalable machine learning, data analysis algorithms, sampling techniques, and privacy and ethical issues. Covers computational platforms supporting Big Data applications Addresses key principles underlying Big Data computing Examines key developments supporting next generation Big Data platforms Explores the challenges in Big Data computing and ways to overcome them Contains expert contributors from both academia and industry

Practical Graph Mining with R

Author: Nagiza F. Samatova,William Hendrix,John Jenkins,Kanchana Padmanabhan,Arpan Chakraborty

Publisher: CRC Press

ISBN: 1439860858

Category: Business & Economics

Page: 495

View: 7982

Discover Novel and Insightful Knowledge from Data Represented as a Graph Practical Graph Mining with R presents a "do-it-yourself" approach to extracting interesting patterns from graph data. It covers many basic and advanced techniques for the identification of anomalous or frequently recurring patterns in a graph, the discovery of groups or clusters of nodes that share common patterns of attributes and relationships, the extraction of patterns that distinguish one category of graphs from another, and the use of those patterns to predict the category of new graphs. Hands-On Application of Graph Data Mining Each chapter in the book focuses on a graph mining task, such as link analysis, cluster analysis, and classification. Through applications using real data sets, the book demonstrates how computational techniques can help solve real-world problems. The applications covered include network intrusion detection, tumor cell diagnostics, face recognition, predictive toxicology, mining metabolic and protein-protein interaction networks, and community detection in social networks. Develops Intuition through Easy-to-Follow Examples and Rigorous Mathematical Foundations Every algorithm and example is accompanied with R code. This allows readers to see how the algorithmic techniques correspond to the process of graph data analysis and to use the graph mining techniques in practice. The text also gives a rigorous, formal explanation of the underlying mathematics of each technique. Makes Graph Mining Accessible to Various Levels of Expertise Assuming no prior knowledge of mathematics or data mining, this self-contained book is accessible to students, researchers, and practitioners of graph data mining. It is suitable as a primary textbook for graph mining or as a supplement to a standard data mining course. It can also be used as a reference for researchers in computer, information, and computational science as well as a handy guide for data analytics practitioners.

One-to-One Personalization in the Age of Machine Learning

Harnessing Data to Power Great Customer Experiences

Author: Karl Wirth,Katie Sweet

Publisher: BookBaby

ISBN: 0999369423

Category: Business & Economics

Page: 146

View: 4038

In a world cluttered with messages competing for people’s attention all of the time, marketers must surface relevant information if they want to capture the attention of their consumers or business buyers. And as consumers experience personalized experiences from other companies like Amazon, Netflix and Spotify, they grow to expect it from all the other companies they interact with, regardless of industry. One-to-one personalization is about tailoring an experience to a visitor or customer at the individual level. The experience could be on a website, mobile app, email, in-person, or any other channel where a person interacts with your brand or company. In contrast to a one-to-all experience (one that is the same for everyone) or a one-to-many experience (one that is targeted to a segment or group of people), a one-to-one experience is truly unique for each person. While marketers have dreamed of delivering one-to-one experiences for over 25 years, it has not been possible without machine learning. Machine learning can combine many different sources of data, draw insights about what that data says about an individual, and determine the most relevant experience to deliver — in a far more scalable way than has ever been possible in the past In One-to-One Personalization in the Age of Machine Learning, discover what one-to-one personalization is all about, how it has evolved and what the future entails. Learn how it's driven by machine learning, delivered across channels and powered by in-depth customer data. Get inspired by the potential for your business and gain insights on how to develop your own personalization strategy and program. Discover how to turn the one-to-one dream into a reality.

The Mathematical Corporation

Where Machine Intelligence and Human Ingenuity Achieve the Impossible

Author: Josh Sullivan,Angela Zutavern

Publisher: PublicAffairs

ISBN: 1610397894

Category: Business & Economics

Page: 304

View: 600

The most powerful weapon in business today is the alliance between the mathematical smarts of machines and the imaginative human intellect of great leaders. Together they make the mathematical corporation, the business model of the future. We are at a once-in-a-decade breaking point similar to the quality revolution of the 1980s and the dawn of the internet age in the 1990s: leaders must transform how they run their organizations, or competitors will bring them crashing to earth--often overnight. Mathematical corporations--the organizations that will master the future--will outcompete high-flying rivals by merging the best of human ingenuity with machine intelligence. While smart machines are weapon number one for organizations, leaders are still the drivers of breakthroughs. Only they can ask crucial questions to capitalize on business opportunities newly discovered in oceans of data. This dynamic combination will make possible the fulfillment of missions that once seemed out of reach, even impossible to attain. Josh Sullivan and Angela Zutavern's extraordinary examples include the entrepreneur who upended preventive health care, the oceanographer who transformed fisheries management, and the pharmaceutical company that used algorithm-driven optimization to boost vaccine yields. Together they offer a profoundly optimistic vision for a dazzling new phase in business, and a playbook for how smart companies can manage the essential combination of human and machine.

Pattern Recognition and Machine Learning

Author: Christopher M. Bishop

Publisher: Springer

ISBN: 9781493938438

Category: Computers

Page: 738

View: 5182

This is the first textbook on pattern recognition to present the Bayesian viewpoint. The book presents approximate inference algorithms that permit fast approximate answers in situations where exact answers are not feasible. It uses graphical models to describe probability distributions when no other books apply graphical models to machine learning. No previous knowledge of pattern recognition or machine learning concepts is assumed. Familiarity with multivariate calculus and basic linear algebra is required, 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.

Machine Learning and Knowledge Discovery in Databases

European Conference, ECML PKDD 2013, Prague, Czech Republic, September 23-27, 2013, Proceedings

Author: Hendrik Blockeel,Kristian Kersting,Siegfried Nijssen,Filip Zelezny

Publisher: Springer

ISBN: 3642409946

Category: Computers

Page: 691

View: 661

This three-volume set LNAI 8188, 8189 and 8190 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2013, held in Prague, Czech Republic, in September 2013. The 111 revised research papers presented together with 5 invited talks were carefully reviewed and selected from 447 submissions. The papers are organized in topical sections on reinforcement learning; Markov decision processes; active learning and optimization; learning from sequences; time series and spatio-temporal data; data streams; graphs and networks; social network analysis; natural language processing and information extraction; ranking and recommender systems; matrix and tensor analysis; structured output prediction, multi-label and multi-task learning; transfer learning; bayesian learning; graphical models; nearest-neighbor methods; ensembles; statistical learning; semi-supervised learning; unsupervised learning; subgroup discovery, outlier detection and anomaly detection; privacy and security; evaluation; applications; and medical applications.

Art of Doing Science and Engineering

Learning to Learn

Author: Richard R. Hamming

Publisher: CRC Press

ISBN: 1482283190

Category: Technology & Engineering

Page: 376

View: 9047

Highly effective thinking is an art that engineers and scientists can be taught to develop. By presenting actual experiences and analyzing them as they are described, the author conveys the developmental thought processes employed and shows a style of thinking that leads to successful results is something that can be learned. Along with spectacular successes, the author also conveys how failures contributed to shaping the thought processes. Provides the reader with a style of thinking that will enhance a person's ability to function as a problem-solver of complex technical issues. Consists of a collection of stories about the author's participation in significant discoveries, relating how those discoveries came about and, most importantly, provides analysis about the thought processes and reasoning that took place as the author and his associates progressed through engineering problems.

Next Generation Biomonitoring:

Author: N.A

Publisher: Academic Press

ISBN: 0128139501

Category: Science

Page: 314

View: 1028

Ecological Biomonitoring, Volume 58, the latest release in the Advances in Ecological Research series, is the first part of a thematic on ecological biomonitoring, including specific chapters that cover Aquatic volatile metabolomics – using trace gases to examine ecological processes, Next generation approaches to rapid monitoring Bio-aerosol and the link between human health and environmental microbiology, NGB in Canadian wetlands, Monitoring the biodiversity and functioning of terrestrial systems via high resolution trace gas fluxes, and Computational approaches to gathering biomonitoring data from social media platforms: a superior solution to next generation biomonitoring challenges. Provides information that relates to a thorough understanding of the field Deals with topical and important reviews on the physiology, populations and communities of plants and animals

Mahout in Action

Author: Sean Owen,Robin Anil,Ted Dunning

Publisher: Manning Publications

ISBN: 9781935182689

Category: Computers

Page: 387

View: 9997

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.

The Master Algorithm

How the Quest for the Ultimate Learning Machine Will Remake Our World

Author: Pedro Domingos

Publisher: Basic Books

ISBN: 0465061923

Category: Computers

Page: 352

View: 9816

"Wonderfully erudite, humorous, and easy to read." --KDNuggets In the world's top research labs and universities, the race is on to invent the ultimate learning algorithm: one capable of discovering any knowledge from data, and doing anything we want, before we even ask. In The Master Algorithm, Pedro Domingos lifts the veil to give us a peek inside the learning machines that power Google, Amazon, and your smartphone. He assembles a blueprint for the future universal learner-the Master Algorithm-and discusses what it will mean for business, science, and society. If data-ism is today's philosophy, this book is its bible.