Data Science and Machine Learning: Mathematical and Statistical Methods (Chapman & Hall/Crc Machine Learning & Pattern Recognition) Hardcover – 26 Nov. 2019
By 作者:Dirk P. Kroese (Author), Zdravko Botev (Author), Thomas Taimre (Author), Radislav Vaisman (Author)
pages 页数: 532 pages
Publisher Finelybook 出版社: Chapman and Hall/CRC; 1 edition (26 Nov. 2019)
Language 语言: English
The Book Description robot was collected from Amazon and arranged by Finelybook
“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 作者: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.
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.
Data Science and Machine Learning 9781138492530.zip