Data Science and Machine Learning
Zdravko Botev
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Add to basketSold by PBShop.store US, Wood Dale, IL, U.S.A.
AbeBooks Seller since 7 April 2005
Condition: New
Quantity: 2 available
Add to basketNew Book. Shipped from UK. Established seller since 2000.
Seller Inventory # GB-9781032488684
Praise for the first edition:
“In nine succinct but information-packed chapters, the authors provide a logically structured and robust introduction to the mathematical and statistical methods underpinning the still-evolving field of AI and data science.”
- Joacim Rocklöv and Albert A. Gayle, International Journal of Epidemiology, Volume 49, Issue 6
“This book organizes the algorithms clearly and cleverly. The way the Python code was written follows the algorithm closely―very useful for readers who wish to understand the rationale and flow of the background knowledge.”
- Yin-Ju Lai and Chuhsing Kate Hsiao, Biometrics, Volume 77, Issue 4
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.
New in the Second Edition
This expanded edition provides updates across key areas of statistical learning:
Key Features:
Zdravko I. Botev, PhD, is the pioneer of several modern statistical methodologies, including the diffusion kernel density estimator, the generalized splitting method for rare-event simulation, the bandwidth perturbation matching method, the regenerative rejection sampling method, and the klimax method for feature selection. His contributions to computational statistics and data science have been recognized with honours such as the Christopher Heyde Medal from the Australian Academy of Science and the Gavin Brown Prize from the Australian Mathematical Society.
Dirk P. Kroese, PhD, is an Emeritus Professor in Mathematics and Statistics at the University of Queensland. He is known for his significant contributions to the fields of applied probability, mathematical statistics, machine learning, and Monte Carlo methods. He has published over 140 articles and 7 books. He is a pioneer of the well-known Cross-Entropy (CE) method, which is being used around the world to help solve difficult estimation and optimization problems in science, engineering, and finance.
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).
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