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Data Science and Machine Learning: Mathematical and Statistical Methods
히트도서
판매가격 65,000원
저자 Dirk P. Kroese, Zdravko Botev, Thomas Taimre, Radislav Vaisman
도서종류 외국도서
출판사 CRC
발행언어 영어
발행일 2019
페이지수 532
ISBN 9781138492530
배송비결제 주문시 결제
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    Overview

    "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.

    The Authors:

    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 BotevPhD, 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.


         

    Table of Contents



    Preface

    Notation

    Importing, Summarizing, and Visualizing Data

    Statistical Learning

    Monte Carlo Methods

    Unsupervised Learning

    Regression

    Regularization and Kernel Methods

    Classification

    Decision Trees and Ensemble Methods

    Multivariate Differentiation and Optimization

    Probability and Statistics

    Python Primer

    Bibliography

         

    Index

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