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15 Math Concepts Every Data Scientist Should Know

You're reading from   15 Math Concepts Every Data Scientist Should Know Understand and learn how to apply the math behind data science algorithms

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Product type Paperback
Published in Aug 2024
Publisher Packt
ISBN-13 9781837634187
Length 510 pages
Edition 1st Edition
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Author (1):
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David Hoyle David Hoyle
Author Profile Icon David Hoyle
David Hoyle
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Table of Contents (21) Chapters Close

Preface 1. Part 1: Essential Concepts
2. Chapter 1: Recap of Mathematical Notation and Terminology FREE CHAPTER 3. Chapter 2: Random Variables and Probability Distributions 4. Chapter 3: Matrices and Linear Algebra 5. Chapter 4: Loss Functions and Optimization 6. Chapter 5: Probabilistic Modeling 7. Part 2: Intermediate Concepts
8. Chapter 6: Time Series and Forecasting 9. Chapter 7: Hypothesis Testing 10. Chapter 8: Model Complexity 11. Chapter 9: Function Decomposition 12. Chapter 10: Network Analysis 13. Part 3: Selected Advanced Concepts
14. Chapter 11: Dynamical Systems 15. Chapter 12: Kernel Methods 16. Chapter 13: Information Theory 17. Chapter 14: Non-Parametric Bayesian Methods 18. Chapter 15: Random Matrices 19. Index 20. Other Books You May Enjoy

Using random matrices to represent interactions in large-scale systems

In Chapter 10, we encountered the adjacency matrix method for representing a network. A network also represents a set of components, the nodes, and the network edges can be used to represent the pairwise interactions between the nodes. Figure 15.1 gives an example of a network and its adjacency matrix representation.

Figure 15.1: Network interactions represented as a matrix

Figure 15.1: Network interactions represented as a matrix

It is natural to use a matrix to represent pairwise interactions between elements of any interacting system and not just between nodes in a network. Some of these systems may be physical, such as particles interacting via some force. They may also be non-physical, where the components of the system do not directly exert forces on each other but may influence each other, such as correlations between the share prices of different companies listed on a stock market.

Many of the systems that we study can be large...

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