Data Science and Machine Learning: Mathematical and Statistical Methods

1st Edition

Dirk P. Kroese, Zdravko Botev, Thomas Taimre, Radislav Vaisman

Chapman and Hall/CRC
November 26, 2019 Forthcoming
Textbook - 531 Pages
ISBN 9781138492530 - CAT# K350147
Series: Chapman & Hall/CRC Machine Learning & Pattern Recognition

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Summary

In our present world of automation, cloud computing, algorithms, Artificial Intelligence, and Big Data, few topics are as relevant as Data Science and Machine Learning. Their recent popularity lies not only in their applicability to real-life situations, but also in their natural blending of many different disciplines, including mathematics, statistics, computer science, and engineering.

The purpose of this book is to provide an accessible, yet comprehensive, account of Data Science and Machine Learning. It is intended for anyone 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. Our viewpoint is that computer languages come and go, but the underlying key ideas and algorithms will remain and will form the basis for future progress.

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 both pseudo and actual code
  • Full color throughout

Dirk P. Kroese, PhD, is a Professor of Mathematics and Statistics at The University of Queensland. He has published over 120 articles and five books in a wide range of areas in mathematics, statistics, data science, machine learning, and Monte Carlo methods. He is a pioneer of the well-known Cross-Entropy method—an adaptive Monte Carlo technique, which is being used around the world to help solve difficult estimation and optimization problems in science, engineering, and finance.

Zdravko Botev, PhD, is an Australian Mathematical Science Institute Lecturer in Data Science and Machine Learning with an appointment at the University of New South Wales in Sydney, Australia. He is the recipient of the 2018 Christopher Heyde Medal of the Australian Academy of Science for distinguished research in the Mathematical Sciences.

Thomas Taimre, PhD, is a Senior Lecturer of Mathematics and Statistics at The University of Queensland. His research interests range from applied probability and Monte Carlo methods to applied physics and the remarkably universal self-mixing effect in lasers. He has published over 100 articles, holds a patent, and is the coauthor of Handbook of Monte Carlo Methods (Wiley).

Radislav Vaisman, PhD, is a Lecturer of Mathematics and Statistics at The University of Queensland. His research interests lie at the intersection of applied probability, machine learning, and computer science. He has published over 20 articles and two books.

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