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020 _a9783031564314
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024 7 _a10.1007/978-3-031-56431-4
_2doi
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072 7 _aMAT029000
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082 0 4 _a006.31
_223
100 1 _aMichelucci, Umberto.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_9101806
245 1 0 _aFundamental Mathematical Concepts for Machine Learning in Science
_h[electronic resource] /
_cby Umberto Michelucci.
250 _a1st ed. 2024.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2024.
300 _aXVII, 249 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
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505 0 _a1. 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.
520 _aThis 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, maintaining rigorous mathematical integrity. For readers more versed in mathematics, the book includes advanced sections that are not prerequisites for the initial reading. It ensures concepts are clearly defined and theorems are proven where it's pertinent. Machine learning transcends the mere implementation and training of algorithms; it encompasses the broader challenges of constructing robust datasets, model validation, addressing imbalanced datasets, and fine-tuning hyperparameters. These topics are thoroughly examined within the text, along with the theoretical foundations underlying these methods. Rather than concentrating on particular algorithms this book focuses on the comprehensive concepts and theories essential for their application. It stands as an indispensable resource for any scientist keen on integrating machine learning effectively into their research. Numerous texts delve into the technical execution of machine learning algorithms, often overlooking the foundational concepts vital for fully grasping these methods. This leads to a gap in using these algorithms effectively across diverse disciplines. For instance, a firm grasp of calculus is imperative to comprehend the training processes of algorithms and neural networks, while linear algebra is essential for the application and efficient training of various algorithms, including neural networks. Absent a solid mathematical base, machine learning applications may be, at best, cursory, or at worst, fundamentally flawed. This book lays the foundation for a comprehensive understanding of machine learning algorithms and approaches. .
650 0 _aMachine learning.
_91831
650 0 _aBioinformatics.
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650 0 _aMathematical physics.
_911013
650 0 _aComputer simulation.
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650 0 _aBioengineering.
_93883
650 0 _aComputational intelligence.
_97716
650 1 4 _aMachine Learning.
_91831
650 2 4 _aBioinformatics.
_99561
650 2 4 _aComputational Physics and Simulations.
_984758
650 2 4 _aComputational and Systems Biology.
_931619
650 2 4 _aBiological and Physical Engineering.
_986102
650 2 4 _aComputational Intelligence.
_97716
710 2 _aSpringerLink (Online service)
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773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
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776 0 8 _iPrinted edition:
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776 0 8 _iPrinted edition:
_z9783031564338
856 4 0 _uhttps://doi.org/10.1007/978-3-031-56431-4
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