03980nam a22005055i 4500001001800000003000900018005001700027007001500044008004100059020001800100024003500118050001400153072001700167072002300184072001600207100008200223245011900305250001800424264007500442300003500517336002600552337002600578338003600604347002400640505032500664520192900989650002202918650002002940650002602960650002502986650002003011650003203031650002203063650002003085650004303105650003903148650004103187650003203228710003403260773002603294776003603320776003603356776003603392856004603428978-3-031-56431-4DE-He21320251030112046.0cr nn 008mamaa240517s2024    sz |    s    |||| 0|eng d  a97830315643147 a10.1007/978-3-031-56431-42doi 4aQ325.5-.7 7aUYQM2bicssc 7aMAT0290002bisacsh 7aUYQM2thema1 aMichelucci, Umberto.eauthor.4aut4http://id.loc.gov/vocabulary/relators/aut10aFundamental Mathematical Concepts for Machine Learning in Scienceh[electronic resource] /cby Umberto Michelucci.  a1st ed. 2024. 1aCham :bSpringer International Publishing :bImprint: Springer,c2024.  aXVII, 249 p.bonline resource.  atextbtxt2rdacontent  acomputerbc2rdamedia  aonline resourcebcr2rdacarrier  atext filebPDF2rda0 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.  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. . 0aMachine learning. 0aBioinformatics. 0aMathematical physics. 0aComputer simulation. 0aBioengineering. 0aComputational intelligence.14aMachine Learning.24aBioinformatics.24aComputational Physics and Simulations.24aComputational and Systems Biology.24aBiological and Physical Engineering.24aComputational Intelligence.2 aSpringerLink (Online service)0 tSpringer Nature eBook08iPrinted edition:z978303156430708iPrinted edition:z978303156432108iPrinted edition:z978303156433840uhttps://doi.org/10.1007/978-3-031-56431-4