Название: Statistical Prediction and Machine Learning Автор: John Tuhao Chen, Lincy Y. Chen, Clement Lee Издательство: CRC Press Год: 2024 Страниц: 315 Язык: английский Формат: pdf (true) Размер: 10.1 MB
Written by an experienced statistics educator and two data scientists, this book unifies conventional statistical thinking and contemporary Machine Learning framework into a single overarching umbrella over Data Science. The book is designed to bridge the knowledge gap between conventional statistics and Machine Learning. It provides an accessible approach for readers with a basic statistics background to develop a mastery of Machine Learning. The book starts with elucidating examples in Chapter 1 and fundamentals on refined optimization in Chapter 2, which are followed by common supervised learning methods such as regressions, classification, support vector machines, tree algorithms, and range regressions. After a discussion on unsupervised learning methods, it includes a chapter on unsupervised learning and a chapter on statistical learning with data sequentially or simultaneously from multiple resources.
When addressing practical problems such as high dimensional inference, Machine Learning often relies on computer intensive algorithms. Many of the underlying thought processes and methodologies have been well-developed but are still fundamentally based in the conventional data analysis framework. One of the major challenges underpinning modern Machine Learning stems from the gap between the conventional model-based inference and data-driven learning algorithms. The knowledge gap hinders practitioners (especially students, researchers, data analysts, or consultants) from truly mastering and correctly applying Machine Learning skills in Data Science.
This book is addressed to practitioners in Data Science, but it is also suitable for upper-level undergraduate students and entry-level graduate students who are interested in obtaining a more thorough comprehension of Machine Learning. The potential audience extends to data scientists who are interested in more insightful interpretations of raw outputs generated from Machine Learning. The materials of the book originate from the first author’s lecture notes of a one-semester Machine Learning course taught at the University of California Berkeley.
Key Features:
Unifies conventional model-based framework and contemporary data-driven methods into a single overarching umbrella over Data Science. Includes real-life medical applications in hypertension, stroke, diabetes, thrombolysis, aspirin efficacy. Integrates statistical theory with machine learning algorithms. Includes potential methodological developments in Data Science.
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