Machine Learning for the Quantified Self

Machine Learning for the Quantified Self
Author: Mark Hoogendoorn
Publisher: Springer
Total Pages: 239
Release: 2017-09-28
Genre: Technology & Engineering
ISBN: 3319663089

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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

Self-Tracking
Author: Gina Neff
Publisher: MIT Press
Total Pages: 247
Release: 2016-06-24
Genre: Technology & Engineering
ISBN: 0262529122

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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.

The Qualified Self

The Qualified Self
Author: Lee Humphreys
Publisher: MIT Press
Total Pages: 200
Release: 2018-04-20
Genre: Social Science
ISBN: 0262346265

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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.

The Quantified Self in Precarity

The Quantified Self in Precarity
Author: Phoebe V. Moore
Publisher: Routledge
Total Pages: 294
Release: 2017-09-11
Genre: Business & Economics
ISBN: 1317201604

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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.

Information Theory, Inference and Learning Algorithms

Information Theory, Inference and Learning Algorithms
Author: David J. C. MacKay
Publisher: Cambridge University Press
Total Pages: 694
Release: 2003-09-25
Genre: Computers
ISBN: 9780521642989

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Information theory and inference, taught together in this exciting textbook, lie at the heart of many important areas of modern technology - communication, signal processing, data mining, machine learning, pattern recognition, computational neuroscience, bioinformatics and cryptography. The book introduces theory in tandem with applications. Information theory is taught alongside practical communication systems such as arithmetic coding for data compression and sparse-graph codes for error-correction. Inference techniques, including message-passing algorithms, Monte Carlo methods and variational approximations, are developed alongside applications to clustering, convolutional codes, independent component analysis, and neural networks. Uniquely, the book covers state-of-the-art error-correcting codes, including low-density-parity-check codes, turbo codes, and digital fountain codes - the twenty-first-century standards for satellite communications, disk drives, and data broadcast. Richly illustrated, filled with worked examples and over 400 exercises, some with detailed solutions, the book is ideal for self-learning, and for undergraduate or graduate courses. It also provides an unparalleled entry point for professionals in areas as diverse as computational biology, financial engineering and machine learning.

Automated Machine Learning

Automated Machine Learning
Author: Frank Hutter
Publisher: Springer
Total Pages: 223
Release: 2019-05-17
Genre: Computers
ISBN: 3030053180

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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.

Machine Learning for Subsurface Characterization

Machine Learning for Subsurface Characterization
Author: Siddharth Misra
Publisher: Gulf Professional Publishing
Total Pages: 442
Release: 2019-10-12
Genre: Technology & Engineering
ISBN: 0128177373

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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

Data Science and Machine Learning

Data Science and Machine Learning
Author: Dirk P. Kroese
Publisher: CRC Press
Total Pages: 538
Release: 2019-11-20
Genre: Business & Economics
ISBN: 1000730778

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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

Lifelogging

Lifelogging
Author: Stefan Selke
Publisher: Springer
Total Pages: 368
Release: 2016-06-14
Genre: Social Science
ISBN: 3658131373

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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.