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Data science and machine learning: mathematical and statistical methods

By: Contributor(s): Material type: TextTextSeries: Chapman & Hall/CRC machine learning & pattern recognitionPublication details: CRC Press 2020 Boca RatonDescription: xxi, 511 p. : ill. Includes bibliography and indexISBN:
  • 9781138492530
Subject(s): DDC classification:
  • 006.31 K7D2
Summary: "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. https://www.routledge.com/Data-Science-and-Machine-Learning-Mathematical-and-Statistical-Methods/Kroese-Botev-Taimre-Vaisman/p/book/9781138492530
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Item type Current library Item location Collection Shelving location Call number Status Date due Barcode
Books Vikram Sarabhai Library Rack 4-A / Slot 107 (0 Floor, West Wing) Non-fiction General Stacks 006.31 K7D2 (Browse shelf(Opens below)) Available 204623

Table of Contents

1. Importing, Summarizing, and Visualizing Data

2. Statistical Learning

3. Monte Carlo Methods

4. Unsupervised Learning

5. Regression

6. Regularization and Kernel Methods

7. Classification

8. Decision Trees and Ensemble Methods

9. Deep Learning
A. Linear Algebra and Functional Analysis
B. Multivariate Differentiation and Optimization
C. Random Experiments and Probability Spaces
D. Python Primer

Bibliography
Index

"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.

https://www.routledge.com/Data-Science-and-Machine-Learning-Mathematical-and-Statistical-Methods/Kroese-Botev-Taimre-Vaisman/p/book/9781138492530

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