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Practical Discrete Mathematics

You're reading from   Practical Discrete Mathematics Discover math principles that fuel algorithms for computer science and machine learning with Python

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Product type Paperback
Published in Feb 2021
Publisher Packt
ISBN-13 9781838983147
Length 330 pages
Edition 1st Edition
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Authors (2):
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Ryan T. White Ryan T. White
Author Profile Icon Ryan T. White
Ryan T. White
Archana Tikayat Ray Archana Tikayat Ray
Author Profile Icon Archana Tikayat Ray
Archana Tikayat Ray
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Table of Contents (17) Chapters Close

Preface 1. Part I – Basic Concepts of Discrete Math
2. Chapter 1: Key Concepts, Notation, Set Theory, Relations, and Functions FREE CHAPTER 3. Chapter 2: Formal Logic and Constructing Mathematical Proofs 4. Chapter 3: Computing with Base-n Numbers 5. Chapter 4: Combinatorics Using SciPy 6. Chapter 5: Elements of Discrete Probability 7. Part II – Implementing Discrete Mathematics in Data and Computer Science
8. Chapter 6: Computational Algorithms in Linear Algebra 9. Chapter 7: Computational Requirements for Algorithms 10. Chapter 8: Storage and Feature Extraction of Graphs, Trees, and Networks 11. Chapter 9: Searching Data Structures and Finding Shortest Paths 12. Part III – Real-World Applications of Discrete Mathematics
13. Chapter 10: Regression Analysis with NumPy and Scikit-Learn 14. Chapter 11: Web Searches with PageRank 15. Chapter 12: Principal Component Analysis with Scikit-Learn 16. Other Books You May Enjoy

The basics of discrete probability

As we have said, making predictions or finding probabilities requires careful analysis, so we need a mathematical framework for probability. It will all center around the idea of a random experiment.

Definition – random experiment

A random experiment is any process that has an uncertain outcome.

Simple examples of random experiments are tossing a coin or rolling a die, each of which has an uncertain outcome. These are easy to analyze, but some random experiments are much more difficult, such as predicting tomorrow's weather. Despite the complexity, experts can estimate the chance of each possible result of the random experiment using complex meteorological models, taking into account temperatures, humidity, and other atmospheric data.

Something each example has in common is that there is a random result for each experiment. A coin toss may result in heads or tails. We may roll a 1, 2, 3, 4, 5, or 6 on the die. The weather may...

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