Can I read Data Science and Machine Learning: Mathematical and Statistical Methods (Instructor Solution Manual, Solutions) on EtoBox?

Data Science and Machine Learning: Mathematical and Statistical Methods (Instructor Solution Manual, Solutions) by Dirk P Kroese; Zdravko I Botev; Thomas Taimre; Radislav Vaisman; Taylor & Francis (Londyn) is a nonfiction available to read on EtoBox.

What is Data Science and Machine Learning: Mathematical and Statistical Methods (Instructor Solution Manual, Solutions) about?

"This textbook is a well-rounded, rigorous, and informative work presenting the mathematics behind modern machine learning techniques. It hits all the right notes: the choice of topics is up-to-date and perfect for a course on data science for mathematics students at the advanced undergraduate or early graduate level. This book fills a sorely-needed gap in the existing literature by not sacrificing depth for breadth, presenting proofs of major theorems and subsequent derivations, as well as providing a copious amount of Python code. I only wish a book like this had been around when I first began my journey!" -Nicholas Hoell, University of Toronto "This is a well-written book that provides a deeper dive into data-scientific methods than many introductory texts. The writing is clear, and the text logically builds up regularization, classification, and decision trees. Compared to its probable competitors, it carves out a unique niche. -Adam Loy, Carleton College The purpose of Data Science and Machine Learning: Mathematical and Statistical Methods is to provide an accessible, yet comprehensive textbook intended for students interested in gaining a better understanding of the

Who reads Data Science and Machine Learning: Mathematical and Statistical Methods (Instructor Solution Manual, Solutions)?

Author

Dirk P Kroese; Zdravko I Botev; Thomas Taimre; Radislav Vaisman; Taylor & Francis (Londyn)

Publisher

Chapman and Hall/CRC

Published

2019

Language

EN

ISBN

9781000731071

Category

nonfiction

Subjects

Mathematics, Business, Computer Science