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Machine Learning for Knowledge Discovery with R: Methodologies for Modeling, Inference and Prediction
Machine Learning for Knowledge Discovery with R contains methodologies and examples for statistical modelling, inference, and prediction of data analysis. It includes many recent supervised and unsupervised machine learning methodologies such as recursive partitioning modelling, regularized regression, support vector machine, neural network, clustering, and causal-effect inference. Additionally, it emphasizes statistical thinking of data analysis, use of statistical graphs for data structure exploration, and result presentations. The book includes many real-world data examples from life-science, finance, etc. to illustrate the applications of the methods described therein.

Key Features:

  • Contains statistical theory for the most recent supervised and unsupervised machine learning methodologies.
  • Emphasizes broad statistical thinking, judgment, graphical methods, and collaboration with subject-matter-experts in analysis, interpretation, and presentations.
  • Written by statistical data analysis practitioner for practitioners.

The book is suitable for upper-level-undergraduate or graduate-level data analysis course. It also serves as a useful desk-reference for data analysts in scientific research or industrial applications.

1139124343
Machine Learning for Knowledge Discovery with R: Methodologies for Modeling, Inference and Prediction
Machine Learning for Knowledge Discovery with R contains methodologies and examples for statistical modelling, inference, and prediction of data analysis. It includes many recent supervised and unsupervised machine learning methodologies such as recursive partitioning modelling, regularized regression, support vector machine, neural network, clustering, and causal-effect inference. Additionally, it emphasizes statistical thinking of data analysis, use of statistical graphs for data structure exploration, and result presentations. The book includes many real-world data examples from life-science, finance, etc. to illustrate the applications of the methods described therein.

Key Features:

  • Contains statistical theory for the most recent supervised and unsupervised machine learning methodologies.
  • Emphasizes broad statistical thinking, judgment, graphical methods, and collaboration with subject-matter-experts in analysis, interpretation, and presentations.
  • Written by statistical data analysis practitioner for practitioners.

The book is suitable for upper-level-undergraduate or graduate-level data analysis course. It also serves as a useful desk-reference for data analysts in scientific research or industrial applications.

61.99 In Stock
Machine Learning for Knowledge Discovery with R: Methodologies for Modeling, Inference and Prediction

Machine Learning for Knowledge Discovery with R: Methodologies for Modeling, Inference and Prediction

by Kao-Tai Tsai
Machine Learning for Knowledge Discovery with R: Methodologies for Modeling, Inference and Prediction
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Machine Learning for Knowledge Discovery with R: Methodologies for Modeling, Inference and Prediction

Machine Learning for Knowledge Discovery with R: Methodologies for Modeling, Inference and Prediction

by Kao-Tai Tsai

Overview

Machine Learning for Knowledge Discovery with R contains methodologies and examples for statistical modelling, inference, and prediction of data analysis. It includes many recent supervised and unsupervised machine learning methodologies such as recursive partitioning modelling, regularized regression, support vector machine, neural network, clustering, and causal-effect inference. Additionally, it emphasizes statistical thinking of data analysis, use of statistical graphs for data structure exploration, and result presentations. The book includes many real-world data examples from life-science, finance, etc. to illustrate the applications of the methods described therein.

Key Features:

  • Contains statistical theory for the most recent supervised and unsupervised machine learning methodologies.
  • Emphasizes broad statistical thinking, judgment, graphical methods, and collaboration with subject-matter-experts in analysis, interpretation, and presentations.
  • Written by statistical data analysis practitioner for practitioners.

The book is suitable for upper-level-undergraduate or graduate-level data analysis course. It also serves as a useful desk-reference for data analysts in scientific research or industrial applications.

Product Details

ISBN-13: 9781032071596
Publisher: CRC Press
Publication date: 09/25/2023
Pages: 260
Product dimensions: 6.12(w) x 9.19(h) x (d)

About the Author

Kao-Tai Tsai obtained his Ph.D. in Mathematical Statistics from University of California, San Diego and had worked at AT&T Bell Laboratories to conduct statistical research, modelling, and exploratory data analysis. After that, he joined the US FDA and later pharmaceutical companies focusing on biostatistics, clinical trial research and data analysis to address the unmet needs in human health.

Table of Contents

1. Statistical Data Analysis. 2. Examining Data Distribution. 3. Regression with Shrinkage. 4. Recursive Partitioning Modeling. 5. Support Vector Machines. 6. Cluster Analysis. 7. Neural Networks. 8. Causal Inference and Matching. 9. Business and Commercial Data Modeling. 10. Analysis of Response Profiles.
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