Statistical Regression And Classification: From Linear Models To Machine Learning

Norman Matloff

ISBN 10: 113806646X / ISBN 13: 9781138066465

Edité par Chapman and Hall/CRC, 2017

A propos de cet article

Brand New. Soft Cover International Edition. Different ISBN and Cover Image. Priced lower than the standard editions which is usually intended to make them more affordable for students abroad. The core content of the book is generally the same as the standard edition. The country selling restrictions may be printed on the book but is no problem for the self-use. This Item maybe shipped from US or any other country as we have multiple locations worldwide.

N° de réf. du vendeur ABNR-6039

Détails bibliographiques

• Titre: Statistical Regression And Classification: From Linear Models To Machine Learning
• Éditeur: Chapman and Hall/CRC
• Date de parution: 2017
• Langue: anglais
• ISBN à 10 chiffres: 113806646X
• ISBN à 13 chiffres: 9781138066465
• Reliure: Couverture souple
• Édition: Edition internationale
• État de l'article: New

A propos de ce titre

Synopsis

This text provides a modern introduction to regression and classification with an emphasis on big data and R. Each chapter is partitioned into a main body section and an extras section. The main body uses math stat very sparingly and always in the context of something concrete, which means that readers can skip the math stat content entirely if they wish. The extras section is for those who feel comfortable with analysis using math stat.

À propos de l?auteur

Norman Matloff is a professor of computer science at the University of California, Davis, and was a founder of the Statistics Department at that institution. Statistical Regression and Classification: From Linear Models to Machine Learning was awarded the 2017 Ziegel Award for the best book reviewed in Technometrics in 2017. His current research focus is on recommender systems, and applications of regression methods to small area estimation and bias reduction in observational studies. He is on the editorial boards of the Journal of Statistical Computation and the R Journal. An award-winning teacher, he is the author of The Art of R Programming and Parallel Computation in Data Science: With Examples in R, C++ and CUDA.