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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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Autore principale: Michelucci, Umberto (Autore)
Ente Autore: SpringerLink (Online service)
Natura: Elettronico eBook
Lingua:inglese
Pubblicazione: Cham : Springer International Publishing : Imprint: Springer, 2024.
Edizione:1st ed. 2024.
Soggetti:
Machine learning.
Bioinformatics.
Mathematical physics.
Computer simulation.
Bioengineering.
Computational intelligence.
Machine Learning.
Computational Physics and Simulations.
Computational and Systems Biology.
Biological and Physical Engineering.
Computational Intelligence.
Accesso online:https://doi.org/10.1007/978-3-031-56431-4
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Sommario:
  • 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.

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