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Data Science and Machine Learning: Mathematical and Statistical Methods
"This textbook is a well-rounded, rigorous, and informative work presenting the mathematics behind modern machine learning techniques. It hits all the right 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 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
Further Resources can be found on the authors
"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 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
Further Resources can be found on the authors
527 pages, Kindle Edition
Published November 20, 2019
4 people are currently reading
44 people want to read
About the author
Dirk P Kroese
3 booksDirk Pieter Kroese is a Dutch-Australian mathematician and statistician, and Professor at the University of Queensland. He is known for several contributions to applied probability, kernel density estimation, Monte Carlo methods and rare event simulation.
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Displaying 1 of 1 review
June 30, 2020
This is by far the most useful book I’ve read on Data Science and Machine Learning. I’m teaching myself Python, Statistics and Data Science in my spare time, and the book has helped me greatly.
The book clearly explains in depth the maths and behind the algorithms, and then follows up with examples and exercises in Python. And it looks lovely, with beautifully rendered colour diagrams.
I’ve read a few books on the subject, but have found them to be too superficial. This is much more rigorous, and I expect it will be my main study text for quite some time.
The book clearly explains in depth the maths and behind the algorithms, and then follows up with examples and exercises in Python. And it looks lovely, with beautifully rendered colour diagrams.
I’ve read a few books on the subject, but have found them to be too superficial. This is much more rigorous, and I expect it will be my main study text for quite some time.
Displaying 1 of 1 review