MACHINE LEARNING FOR KNOWLEDGE DISCOVERY WITH R

Tsai, Kao-Tai

ISBN 10: 1032071591 / ISBN 13: 9781032071596

Edité par Chapman and Hall/CRC, 2023

A propos de cet article

This is a Brand-new US Edition. This Item may be shipped from US or any other country as we have multiple locations worldwide.

N° de réf. du vendeur ABNR-14767

Détails bibliographiques

• Titre: MACHINE LEARNING FOR KNOWLEDGE DISCOVERY WITH R
• Éditeur: Chapman and Hall/CRC
• Date de parution: 2023
• Langue: anglais
• ISBN à 10 chiffres: 1032071591
• ISBN à 13 chiffres: 9781032071596
• Reliure: Couverture souple
• État de l'article: New

A propos de ce titre

Synopsis

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.

À propos de l?auteur

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.