| 書(shū)目名稱 | Mathematical Introduction to Data Science | | 編輯 | Sven A. Wegner | | 視頻video | http://file.papertrans.cn/627/626183/626183.mp4 | | 概述 | Provides a concise and understandable introduction to the mathematics of data science.Guides the reader by the central principles of the subject.Mathematically precise and focussed on the application | | 圖書(shū)封面 |  | | 描述 | .This textbook is intended for students of mathematics who have completed the foundational courses of their undergraduate studies and now want to specialize 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 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.. | | 出版日期 | Textbook 2024 | | 關(guān)鍵詞 | Machine Learning; Data Science; Support Vector Machines; Deep learning; Perceptron; Vector support machin | | 版次 | 1 | | doi | https://doi.org/10.1007/978-3-662-69426-8 | | isbn_softcover | 978-3-662-69425-1 | | isbn_ebook | 978-3-662-69426-8 | | copyright | The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer-Verlag GmbH, DE |
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