Mark Hoogendoorn; Burkhardt Funk Machine Learning for the Quantified Self 9783319663074

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Автор: Mark Hoogendoorn; Burkhardt Funk
Название: Machine Learning for the Quantified Self
ISBN: 9783319663074
Издательство: Springer
Классификация:

Теория кодирования и криптография

Искусственный интеллект

Машинное обучение

ISBN-10: 3319663070
Обложка/Формат: Hardcover
Страницы: 231
Вес: 0.53 кг.
Дата издания: 05.10.2017
Серия: Cognitive Systems Monographs
Язык: English
Издание: 1st ed. 2018
Иллюстрации: 72 illustrations, color; 17 illustrations, black and white; xii, 226 p. 89 illus., 72 illus. in color.
Размер: 165 x 245 x 20
Читательская аудитория: Professional & vocational
Основная тема: Engineering
Подзаголовок: On the Art of Learning from Sensory Data
Ссылка на Издательство: Link
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Поставляется из: Германии
Описание: 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.
Дополнительное описание: Introduction.- Basics of Sensory Data.- Feature Engineering based on Sensory Data.- Predictive Modeling without Notion of Time.- Predictive Modeling with Notion of Time.- Reinforcement Learning to Provide Feedback and Support.- Discussion.

Machine Learning for the Quantified Self, Mark Hoogendoorn; Burkhardt Funk