Fundamental Mathematical Concepts for Machine Learning in Science
This book is for individuals with a scientific background who aspire to apply machine learning within various natural science disciplines—such as physics, chemistry, biology, medicine, psychology and many more. It elucidates core mathematical concepts in an accessible and straightforward manner, mai...
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| Format: | eBook |
| Language: | English |
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Cham
Springer International Publishing
2024, 2024
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| Edition: | 1st ed. 2024 |
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| Collection: | Springer eBooks 2005- - Collection details see MPG.ReNa |
Table of Contents:
- 1. Introduction
- 2. Calculus and Optimisation for Machine Learning
- 3. Linear Algebra
- 4. Statistics and Probability for Machine Learning
- 5. Sampling Theory (a.k.a. Creating a Dataset Properly)
- 6. Model Validation
- 7. Unbalanced Datasets
- 8. Hyperparameter Tuning
- 9. Model Agnostic Feature Importance