Titlebook: Mathematical Introduction to Data Science; Sven A. Wegner Textbook 2024 The Editor(s) (if applicable) and The Author(s), under exclusive l

Calculus

Textbook 2024bors, linear and logistic regression, clustering, best-fit subspaces, principal component analysis, dimensionality reduction, collaborative filtering, perceptron, support vector machines, the kernel method, gradient descent and neural networks..

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Textbook 2024ialize in Data Science and Machine Learning. It introduces the reader to the most important topics in the latter areas focusing on rigorous proofs and a systematic understanding of the underlying ideas...The textbook comes with 121 classroom-tested exercises. Topics covered include .k.-nearest neigh

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-tested exercises. Topics covered include .k.-nearest neighbors, linear and logistic regression, clustering, best-fit subspaces, principal component analysis, dimensionality reduction, collaborative filtering, perceptron, support vector machines, the kernel method, gradient descent and neural networks..978-3-662-69425-1978-3-662-69426-8

Lime石灰

-Nearest Neighbors,ce some preprocessing methods and then generalize the initially mentioned setting of metric spaces to distance measures in order to include cosine similarity and cosine distance into our theory. As examples, we discuss text mining, product reviews, and handwriting recognition.