Data Science and Machine Learning: Mathematical and Statistical Methods

Data Science and Machine Learning: Mathematical and Statistical Methods (Chapman & Hall/Crc Machine Learning & Pattern Recognition) Hardcover – 26 Nov. 2019
by: Dirk P. Kroese ,Zdravko Botev ,Thomas Taimre ,Radislav Vaisman
Print length 页数: 532 pages
Publisher finelybook 出版社: Chapman and Hall/CRC; 1 edition (26 Nov. 2019)
Language 语言: English
ISBN-10: 1138492531
ISBN-13: 9781138492530
Book Description
“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.