Machine Learning For The Quantified Self

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Machine Learning for the Quantified Self

Author : Mark Hoogendoorn
Publisher : Springer
ISBN 13 : 3319663089
Total Pages : 231 pages
Book Rating : 4.3/5 (196 download)

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Book Synopsis Machine Learning for the Quantified Self by : Mark Hoogendoorn

Download or read book Machine Learning for the Quantified Self written by Mark Hoogendoorn and published by Springer. This book was released on 2017-09-28 with total page 231 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book explains the complete loop to effectively use self-tracking data for machine learning. While it focuses on self-tracking data, the techniques explained are also applicable to sensory data in general, making it useful for a wider audience. Discussing concepts drawn from from state-of-the-art scientific literature, it illustrates the approaches using a case study of a rich self-tracking data set. Self-tracking has become part of the modern lifestyle, and the amount of data generated by these devices is so overwhelming that it is difficult to obtain useful insights from it. Luckily, in the domain of artificial intelligence there are techniques that can help out: machine-learning approaches allow this type of data to be analyzed. While there are ample books that explain machine-learning techniques, self-tracking data comes with its own difficulties that require dedicated techniques such as learning over time and across users.

Machine Learning for the Quantified Self

Author : Mark Hoogendoorn
Publisher : Springer
ISBN 13 : 9783319882154
Total Pages : 0 pages
Book Rating : 4.8/5 (821 download)

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Book Synopsis Machine Learning for the Quantified Self by : Mark Hoogendoorn

Download or read book Machine Learning for the Quantified Self written by Mark Hoogendoorn and published by Springer. This book was released on 2018-08-15 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book explains the complete loop to effectively use self-tracking data for machine learning. While it focuses on self-tracking data, the techniques explained are also applicable to sensory data in general, making it useful for a wider audience. Discussing concepts drawn from from state-of-the-art scientific literature, it illustrates the approaches using a case study of a rich self-tracking data set. Self-tracking has become part of the modern lifestyle, and the amount of data generated by these devices is so overwhelming that it is difficult to obtain useful insights from it. Luckily, in the domain of artificial intelligence there are techniques that can help out: machine-learning approaches allow this type of data to be analyzed. While there are ample books that explain machine-learning techniques, self-tracking data comes with its own difficulties that require dedicated techniques such as learning over time and across users.

Machine Learning for the Quantified Self

Author : Mark Hoogendoorn
Publisher : Springer
ISBN 13 : 9783319663074
Total Pages : 231 pages
Book Rating : 4.6/5 (63 download)

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Book Synopsis Machine Learning for the Quantified Self by : Mark Hoogendoorn

Download or read book Machine Learning for the Quantified Self written by Mark Hoogendoorn and published by Springer. This book was released on 2017-10-05 with total page 231 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book explains the complete loop to effectively use self-tracking data for machine learning. While it focuses on self-tracking data, the techniques explained are also applicable to sensory data in general, making it useful for a wider audience. Discussing concepts drawn from from state-of-the-art scientific literature, it illustrates the approaches using a case study of a rich self-tracking data set. Self-tracking has become part of the modern lifestyle, and the amount of data generated by these devices is so overwhelming that it is difficult to obtain useful insights from it. Luckily, in the domain of artificial intelligence there are techniques that can help out: machine-learning approaches allow this type of data to be analyzed. While there are ample books that explain machine-learning techniques, self-tracking data comes with its own difficulties that require dedicated techniques such as learning over time and across users.

Self-Tracking

Author : Gina Neff
Publisher : MIT Press
ISBN 13 : 0262529122
Total Pages : 247 pages
Book Rating : 4.2/5 (625 download)

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Book Synopsis Self-Tracking by : Gina Neff

Download or read book Self-Tracking written by Gina Neff and published by MIT Press. This book was released on 2016-06-24 with total page 247 pages. Available in PDF, EPUB and Kindle. Book excerpt: What happens when people turn their everyday experience into data: an introduction to the essential ideas and key challenges of self-tracking. People keep track. In the eighteenth century, Benjamin Franklin kept charts of time spent and virtues lived up to. Today, people use technology to self-track: hours slept, steps taken, calories consumed, medications administered. Ninety million wearable sensors were shipped in 2014 to help us gather data about our lives. This book examines how people record, analyze, and reflect on this data, looking at the tools they use and the communities they become part of. Gina Neff and Dawn Nafus describe what happens when people turn their everyday experience—in particular, health and wellness-related experience—into data, and offer an introduction to the essential ideas and key challenges of using these technologies. They consider self-tracking as a social and cultural phenomenon, describing not only the use of data as a kind of mirror of the self but also how this enables people to connect to, and learn from, others. Neff and Nafus consider what's at stake: who wants our data and why; the practices of serious self-tracking enthusiasts; the design of commercial self-tracking technology; and how self-tracking can fill gaps in the healthcare system. Today, no one can lead an entirely untracked life. Neff and Nafus show us how to use data in a way that empowers and educates.

Machine Learning for Subsurface Characterization

Author : Siddharth Misra
Publisher : Gulf Professional Publishing
ISBN 13 : 0128177373
Total Pages : 442 pages
Book Rating : 4.1/5 (281 download)

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Book Synopsis Machine Learning for Subsurface Characterization by : Siddharth Misra

Download or read book Machine Learning for Subsurface Characterization written by Siddharth Misra and published by Gulf Professional Publishing. This book was released on 2019-10-12 with total page 442 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine Learning for Subsurface Characterization develops and applies neural networks, random forests, deep learning, unsupervised learning, Bayesian frameworks, and clustering methods for subsurface characterization. Machine learning (ML) focusses on developing computational methods/algorithms that learn to recognize patterns and quantify functional relationships by processing large data sets, also referred to as the "big data." Deep learning (DL) is a subset of machine learning that processes "big data" to construct numerous layers of abstraction to accomplish the learning task. DL methods do not require the manual step of extracting/engineering features; however, it requires us to provide large amounts of data along with high-performance computing to obtain reliable results in a timely manner. This reference helps the engineers, geophysicists, and geoscientists get familiar with data science and analytics terminology relevant to subsurface characterization and demonstrates the use of data-driven methods for outlier detection, geomechanical/electromagnetic characterization, image analysis, fluid saturation estimation, and pore-scale characterization in the subsurface. Learn from 13 practical case studies using field, laboratory, and simulation data Become knowledgeable with data science and analytics terminology relevant to subsurface characterization Learn frameworks, concepts, and methods important for the engineer’s and geoscientist’s toolbox needed to support

The Quantified Self in Precarity

Author : Phoebe V. Moore
Publisher : Routledge
ISBN 13 : 1317201604
Total Pages : 234 pages
Book Rating : 4.3/5 (172 download)

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Book Synopsis The Quantified Self in Precarity by : Phoebe V. Moore

Download or read book The Quantified Self in Precarity written by Phoebe V. Moore and published by Routledge. This book was released on 2017-09-11 with total page 234 pages. Available in PDF, EPUB and Kindle. Book excerpt: Humans are accustomed to being tool bearers, but what happens when machines become tool bearers, calculating human labour via the use of big data and people analytics by metrics? The Quantified Self in Precarity highlights how, whether it be in insecure ‘gig’ work or office work, such digitalisation is not an inevitable process – nor is it one that necessarily improves working conditions. Indeed, through unique research and empirical data, Moore demonstrates how workplace quantification leads to high turnover rates, workplace rationalisation and worker stress and anxiety, with these issues linked to increased rates of subjective and objective precarity. Scientific management asked us to be efficient. Now, we are asked to be agile. But what does this mean for the everyday lives we lead? With a fresh perspective on how technology and the use of technology for management and self-management changes the ‘quantified’, precarious workplace today, The Quantified Self in Precarity will appeal to undergraduate and postgraduate students interested in fields such as Science and Technology, Organisation Management, Sociology and Politics.

The Qualified Self

Author : Lee Humphreys
Publisher : MIT Press
ISBN 13 : 0262346265
Total Pages : 200 pages
Book Rating : 4.2/5 (623 download)

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Book Synopsis The Qualified Self by : Lee Humphreys

Download or read book The Qualified Self written by Lee Humphreys and published by MIT Press. This book was released on 2018-04-20 with total page 200 pages. Available in PDF, EPUB and Kindle. Book excerpt: How sharing the mundane details of daily life did not start with Facebook, Twitter, and YouTube but with pocket diaries, photo albums, and baby books. Social critiques argue that social media have made us narcissistic, that Facebook, Twitter, Instagram, and YouTube are all vehicles for me-promotion. In The Qualified Self, Lee Humphreys offers a different view. She shows that sharing the mundane details of our lives—what we ate for lunch, where we went on vacation, who dropped in for a visit—didn't begin with mobile devices and social media. People have used media to catalog and share their lives for several centuries. Pocket diaries, photo albums, and baby books are the predigital precursors of today's digital and mobile platforms for posting text and images. The ability to take selfies has not turned us into needy narcissists; it's part of a longer story about how people account for everyday life. Humphreys refers to diaries in which eighteenth-century daily life is documented with the brevity and precision of a tweet, and cites a nineteenth-century travel diary in which a young woman complains that her breakfast didn't agree with her. Diaries, Humphreys explains, were often written to be shared with family and friends. Pocket diaries were as mobile as smartphones, allowing the diarist to record life in real time. Humphreys calls this chronicling, in both digital and nondigital forms, media accounting. The sense of self that emerges from media accounting is not the purely statistics-driven “quantified self,” but the more well-rounded qualified self. We come to understand ourselves in a new way through the representations of ourselves that we create to be consumed.

Automated Machine Learning

Author : Frank Hutter
Publisher : Springer
ISBN 13 : 3030053180
Total Pages : 223 pages
Book Rating : 4.0/5 (3 download)

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Book Synopsis Automated Machine Learning by : Frank Hutter

Download or read book Automated Machine Learning written by Frank Hutter and published by Springer. This book was released on 2019-05-17 with total page 223 pages. Available in PDF, EPUB and Kindle. Book excerpt: This open access book presents the first comprehensive overview of general methods in Automated Machine Learning (AutoML), collects descriptions of existing systems based on these methods, and discusses the first series of international challenges of AutoML systems. The recent success of commercial ML applications and the rapid growth of the field has created a high demand for off-the-shelf ML methods that can be used easily and without expert knowledge. However, many of the recent machine learning successes crucially rely on human experts, who manually select appropriate ML architectures (deep learning architectures or more traditional ML workflows) and their hyperparameters. To overcome this problem, the field of AutoML targets a progressive automation of machine learning, based on principles from optimization and machine learning itself. This book serves as a point of entry into this quickly-developing field for researchers and advanced students alike, as well as providing a reference for practitioners aiming to use AutoML in their work.

Information Theory, Inference and Learning Algorithms

Author : David J. C. MacKay
Publisher : Cambridge University Press
ISBN 13 : 9780521642989
Total Pages : 694 pages
Book Rating : 4.6/5 (429 download)

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Book Synopsis Information Theory, Inference and Learning Algorithms by : David J. C. MacKay

Download or read book Information Theory, Inference and Learning Algorithms written by David J. C. MacKay and published by Cambridge University Press. This book was released on 2003-09-25 with total page 694 pages. Available in PDF, EPUB and Kindle. Book excerpt: Table of contents

The Quantified Self

Author : Deborah Lupton
Publisher : John Wiley & Sons
ISBN 13 : 1509500634
Total Pages : 240 pages
Book Rating : 4.5/5 (95 download)

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Book Synopsis The Quantified Self by : Deborah Lupton

Download or read book The Quantified Self written by Deborah Lupton and published by John Wiley & Sons. This book was released on 2016-09-07 with total page 240 pages. Available in PDF, EPUB and Kindle. Book excerpt: With the advent of digital devices and software, self-tracking practices have gained new adherents and have spread into a wide array of social domains. The Quantified Self movement has emerged to promote 'self-knowledge through numbers'. In this groundbreaking book Deborah Lupton critically analyses the social, cultural and political dimensions of contemporary self-tracking and identifies the concepts of selfhood and human embodiment and the value of the data that underpin them. The book incorporates discussion of the consolations and frustrations of self-tracking, as well as about the proliferating ways in which people's personal data are now used beyond their private rationales. Lupton outlines how the information that is generated through self-tracking is taken up and repurposed for commercial, governmental, managerial and research purposes. In the relationship between personal data practices and big data politics, the implications of self-tracking are becoming ever more crucial.

Data Science and Machine Learning

Author : Dirk P. Kroese
Publisher : CRC Press
ISBN 13 : 1000730778
Total Pages : 538 pages
Book Rating : 4.0/5 (7 download)

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Book Synopsis Data Science and Machine Learning by : Dirk P. Kroese

Download or read book Data Science and Machine Learning written by Dirk P. Kroese and published by CRC Press. This book was released on 2019-11-20 with total page 538 pages. Available in PDF, EPUB and Kindle. Book excerpt: Focuses on mathematical understanding Presentation is self-contained, accessible, and comprehensive Full color throughout Extensive list of exercises and worked-out examples Many concrete algorithms with actual code

MATLAB for Machine Learning

Author : Giuseppe Ciaburro
Publisher : Packt Publishing Ltd
ISBN 13 : 1788399390
Total Pages : 374 pages
Book Rating : 4.7/5 (883 download)

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Book Synopsis MATLAB for Machine Learning by : Giuseppe Ciaburro

Download or read book MATLAB for Machine Learning written by Giuseppe Ciaburro and published by Packt Publishing Ltd. This book was released on 2017-08-28 with total page 374 pages. Available in PDF, EPUB and Kindle. Book excerpt: Extract patterns and knowledge from your data in easy way using MATLAB About This Book Get your first steps into machine learning with the help of this easy-to-follow guide Learn regression, clustering, classification, predictive analytics, artificial neural networks and more with MATLAB Understand how your data works and identify hidden layers in the data with the power of machine learning. Who This Book Is For This book is for data analysts, data scientists, students, or anyone who is looking to get started with machine learning and want to build efficient data processing and predicting applications. A mathematical and statistical background will really help in following this book well. What You Will Learn Learn the introductory concepts of machine learning. Discover different ways to transform data using SAS XPORT, import and export tools, Explore the different types of regression techniques such as simple & multiple linear regression, ordinary least squares estimation, correlations and how to apply them to your data. Discover the basics of classification methods and how to implement Naive Bayes algorithm and Decision Trees in the Matlab environment. Uncover how to use clustering methods like hierarchical clustering to grouping data using the similarity measures. Know how to perform data fitting, pattern recognition, and clustering analysis with the help of MATLAB Neural Network Toolbox. Learn feature selection and extraction for dimensionality reduction leading to improved performance. In Detail MATLAB is the language of choice for many researchers and mathematics experts for machine learning. This book will help you build a foundation in machine learning using MATLAB for beginners. You'll start by getting your system ready with t he MATLAB environment for machine learning and you'll see how to easily interact with the Matlab workspace. We'll then move on to data cleansing, mining and analyzing various data types in machine learning and you'll see how to display data values on a plot. Next, you'll get to know about the different types of regression techniques and how to apply them to your data using the MATLAB functions. You'll understand the basic concepts of neural networks and perform data fitting, pattern recognition, and clustering analysis. Finally, you'll explore feature selection and extraction techniques for dimensionality reduction for performance improvement. At the end of the book, you will learn to put it all together into real-world cases covering major machine learning algorithms and be comfortable in performing machine learning with MATLAB. Style and approach The book takes a very comprehensive approach to enhance your understanding of machine learning using MATLAB. Sufficient real-world examples and use cases are included in the book to help you grasp the concepts quickly and apply them easily in your day-to-day work.

Introduction to Machine Learning

Author : Ethem Alpaydin
Publisher : MIT Press
ISBN 13 : 0262028182
Total Pages : 639 pages
Book Rating : 4.2/5 (62 download)

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Book Synopsis Introduction to Machine Learning by : Ethem Alpaydin

Download or read book Introduction to Machine Learning written by Ethem Alpaydin and published by MIT Press. This book was released on 2014-08-22 with total page 639 pages. Available in PDF, EPUB and Kindle. Book excerpt: Introduction -- Supervised learning -- Bayesian decision theory -- Parametric methods -- Multivariate methods -- Dimensionality reduction -- Clustering -- Nonparametric methods -- Decision trees -- Linear discrimination -- Multilayer perceptrons -- Local models -- Kernel machines -- Graphical models -- Brief contents -- Hidden markov models -- Bayesian estimation -- Combining multiple learners -- Reinforcement learning -- Design and analysis of machine learning experiments.

Dive Into Deep Learning

Author : Joanne Quinn
Publisher : Corwin Press
ISBN 13 : 1544385404
Total Pages : 297 pages
Book Rating : 4.5/5 (443 download)

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Book Synopsis Dive Into Deep Learning by : Joanne Quinn

Download or read book Dive Into Deep Learning written by Joanne Quinn and published by Corwin Press. This book was released on 2019-07-15 with total page 297 pages. Available in PDF, EPUB and Kindle. Book excerpt: The leading experts in system change and learning, with their school-based partners around the world, have created this essential companion to their runaway best-seller, Deep Learning: Engage the World Change the World. This hands-on guide provides a roadmap for building capacity in teachers, schools, districts, and systems to design deep learning, measure progress, and assess conditions needed to activate and sustain innovation. Dive Into Deep Learning: Tools for Engagement is rich with resources educators need to construct and drive meaningful deep learning experiences in order to develop the kind of mindset and know-how that is crucial to becoming a problem-solving change agent in our global society. Designed in full color, this easy-to-use guide is loaded with tools, tips, protocols, and real-world examples. It includes: • A framework for deep learning that provides a pathway to develop the six global competencies needed to flourish in a complex world — character, citizenship, collaboration, communication, creativity, and critical thinking. • Learning progressions to help educators analyze student work and measure progress. • Learning design rubrics, templates and examples for incorporating the four elements of learning design: learning partnerships, pedagogical practices, learning environments, and leveraging digital. • Conditions rubrics, teacher self-assessment tools, and planning guides to help educators build, mobilize, and sustain deep learning in schools and districts. Learn about, improve, and expand your world of learning. Put the joy back into learning for students and adults alike. Dive into deep learning to create learning experiences that give purpose, unleash student potential, and transform not only learning, but life itself.

Lifelogging

Author : Stefan Selke
Publisher : Springer
ISBN 13 : 3658131373
Total Pages : 368 pages
Book Rating : 4.6/5 (581 download)

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Book Synopsis Lifelogging by : Stefan Selke

Download or read book Lifelogging written by Stefan Selke and published by Springer. This book was released on 2016-06-14 with total page 368 pages. Available in PDF, EPUB and Kindle. Book excerpt: The following anthology delivers sound analysis to the theoretical classification of the current societal phenomenon - between innovative, world changing and yet disruptive technology, as well as societal and cultural transformation. Lifelogging, digital self-tracking and the real-time chronicling of man’s lifetime, is not only a relevant societal topic in the world of research and academic science these days, but can also be found in literature, cultural pages of the written press and the theatre. The spectrum of Lifelogging ranges from sleep, mood, sex and work logging to Thing and Deathlogging. This leads to several questions: How does one live in a data society? Is “measured” man automatically also “better” man? And if so, what is the cost? Do new categories of reality or principles of social classification develop as a result of Lifelogging? How does the “social view” on things change? The authors in this anthology provide insightful answers to these pressing questions.

Machine Learning and AI for Healthcare

Author : Arjun Panesar
Publisher : Apress
ISBN 13 : 1484237994
Total Pages : 390 pages
Book Rating : 4.4/5 (842 download)

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Book Synopsis Machine Learning and AI for Healthcare by : Arjun Panesar

Download or read book Machine Learning and AI for Healthcare written by Arjun Panesar and published by Apress. This book was released on 2019-02-04 with total page 390 pages. Available in PDF, EPUB and Kindle. Book excerpt: Explore the theory and practical applications of artificial intelligence (AI) and machine learning in healthcare. This book offers a guided tour of machine learning algorithms, architecture design, and applications of learning in healthcare and big data challenges. You’ll discover the ethical implications of healthcare data analytics and the future of AI in population and patient health optimization. You’ll also create a machine learning model, evaluate performance and operationalize its outcomes within your organization. Machine Learning and AI for Healthcare provides techniques on how to apply machine learning within your organization and evaluate the efficacy, suitability, and efficiency of AI applications. These are illustrated through leading case studies, including how chronic disease is being redefined through patient-led data learning and the Internet of Things. What You'll LearnGain a deeper understanding of key machine learning algorithms and their use and implementation within wider healthcare Implement machine learning systems, such as speech recognition and enhanced deep learning/AI Select learning methods/algorithms and tuning for use in healthcare Recognize and prepare for the future of artificial intelligence in healthcare through best practices, feedback loops and intelligent agentsWho This Book Is For Health care professionals interested in how machine learning can be used to develop health intelligence – with the aim of improving patient health, population health and facilitating significant care-payer cost savings.

MATLAB Machine Learning

Author : Michael Paluszek
Publisher : Apress
ISBN 13 : 1484222504
Total Pages : 335 pages
Book Rating : 4.4/5 (842 download)

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Book Synopsis MATLAB Machine Learning by : Michael Paluszek

Download or read book MATLAB Machine Learning written by Michael Paluszek and published by Apress. This book was released on 2016-12-28 with total page 335 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is a comprehensive guide to machine learning with worked examples in MATLAB. It starts with an overview of the history of Artificial Intelligence and automatic control and how the field of machine learning grew from these. It provides descriptions of all major areas in machine learning. The book reviews commercially available packages for machine learning and shows how they fit into the field. The book then shows how MATLAB can be used to solve machine learning problems and how MATLAB graphics can enhance the programmer’s understanding of the results and help users of their software grasp the results. Machine Learning can be very mathematical. The mathematics for each area is introduced in a clear and concise form so that even casual readers can understand the math. Readers from all areas of engineering will see connections to what they know and will learn new technology. The book then provides complete solutions in MATLAB for several important problems in machine learning including face identification, autonomous driving, and data classification. Full source code is provided for all of the examples and applications in the book. What you'll learn: An overview of the field of machine learning Commercial and open source packages in MATLAB How to use MATLAB for programming and building machine learning applications MATLAB graphics for machine learning Practical real world examples in MATLAB for major applications of machine learning in big data Who is this book for: The primary audiences are engineers and engineering students wanting a comprehensive and practical introduction to machine learning.