Data Science and Machine Learning: Mathematical and Statistical Methods (Chapman And Hall/crc Machine Learning And Pattern Recognition Ser.)
By: and and and
Sign Up Now!
Already a Member? Log In
You must be logged into Bookshare to access this title.
Learn about membership options,
or view our freely available titles.
- Synopsis
- "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
- Copyright:
- 2020
Book Details
- Book Quality:
- Publisher Quality
- Book Size:
- 510 Pages
- ISBN-13:
- 9781000731071
- Related ISBNs:
- 9781138492530, 9780367816971
- Publisher:
- CRC Press
- Date of Addition:
- 09/18/23
- Copyrighted By:
- Taylor & Francis Group, LLC
- Adult content:
- No
- Language:
- English
- Has Image Descriptions:
- No
- Categories:
- Nonfiction, Computers and Internet, Business and Finance, Mathematics and Statistics
- Submitted By:
- Bookshare Staff
- Usage Restrictions:
- This is a copyrighted book.
Reviews
Other Books
- by Dirk P. Kroese
- by Zdravko Botev
- by Thomas Taimre
- by Radislav Vaisman
- in Nonfiction
- in Computers and Internet
- in Business and Finance
- in Mathematics and Statistics