Statistical Prediction and Machine Learning

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Synopsis

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.One of the distinct features of this book is the comprehensive coverage of the topics in statistical learning and medical applications. It summarizes the authors’ teaching, research, and consulting experience in which they use data analytics. The illustrating examples and accompanying materials heavily emphasize understanding on data analysis, producing accurate interpretations, and discovering hidden assumptions associated with various methods.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.

Book details

Author:
John Tuhao Chen, Clement Lee, Lincy Y. Chen
ISBN:
9781040096307
Related ISBNs:
9780367332273, 9780429318689, 9781040096338
Publisher:
CRC Press
Pages:
N/A
Reading age:
Not specified
Includes images:
No
Date of addition:
2024-08-05
Usage restrictions:
Copyright
Copyright date:
2024
Copyright by:
N/A 
Adult content:
No
Language:
English
Categories:
Business and Finance, Computers and Internet, Mathematics and Statistics, Nonfiction