Data Science and Machine Learning: Mathematical and Statistical Methods

By Zdravko Botev, Dirk P Kroese, Radislav Vaisman, et al.

"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 mathematics and statistics that underpin the rich variety of ideas and machine learning algorithms in data science.

Key Features:

Focuses on mathematical understanding. Presentation is self-contained, accessible, and comprehensive. Extensive list of exercises and worked-out examples. Many concrete algorithms with Python code. Full color throughout.

Further Resources can be found on the authors website: https: //github.com/DSML-book/Lectures

• Format: Hardcover
• Language: English
• ISBN: 1138492531
• ISBN13: 9781138492530
• Release Date: November 2019
• Publisher: CRC Press
• Length: 538 Pages
• Weight: 3.75 lbs.
• Dimensions: 1.2" x 8.9" x 11.0"