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

— —

Kao-Tai Tsai

Machine Learning for Knowledge Discovery with R

Methodologies for Modeling, Inference and Prediction

Kao-Tai Tsai

$96.99

Paperback

Not in-store but you can order this
How long will it take?

Availability Information

We source books from suppliers in Australia and overseas. For books we don't currently have in stock, the time it takes to get them from our suppliers can vary widely - from a few days to a few months - so we check each book with each supplier to determine the expected time it will take to be supplied to us.

We then advise you accordingly. If the time taken to get any book is too long for you, you can let us know and we will cancel or adjust your order, and refund as required.

To find out the anticipated arrival time for specific items prior to ordering, please contact us by phone or email:

Phone +61 2 9264 3111, or 1800 4 BOOKS (1800 4 26657) if outside Sydney:
option 1 Abbey's Bookshop (Crime, History, Science, Kids & more) • info@abbeys.com.au
option 2 Language Book Centre (ESL & Foreign Languages) • language@abbeys.com.au
option 3 Galaxy Bookshop (Sci-fi, Fantasy, Romance, Graphic Novels) • sf@galaxybooks.com.au

QTY:

English

Chapman & Hall/CRC
25 September 2023

Probability & statistics; Automatic control engineering; Environmental science, engineering & technology

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.

By: Kao-Tai Tsai
Imprint: Chapman & Hall/CRC
Country of Publication: United Kingdom
Dimensions: Height: 234mm, Width: 156mm,
Weight: 453g
ISBN: 9781032071596
ISBN 10: 1032071591
Pages: 244
Publication Date: 25 September 2023
Audience: General/trade , ELT Advanced
Format: Paperback
Publisher's Status: Active

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.

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

"""A knowledgeable applied statistician with good math skills will likely appreciate the brevity of this presentation, as well as its clear descriptions about how to easily apply the methods in R. This book is likely best used as a quick reference for those already familiar with these methods, for when one wants to aplly a particular machine learning method."" Amit K. Chowdhry, University of Rochester, USA, Royal Statistical Society, Series A: Statistics in Society. ""I will definitely recommend this book without any reservation to individuals in data science or associated disciplines that utilize machine learning and predictive modelling strategies for quantitatively making inference of data sets."" - Reuben Adatorwovor, ISCB News, September 2022."