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210407 ||| eng |
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|a 9783030623418
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|a Phillips, Jeff M.
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|a Mathematical Foundations for Data Analysis
|h Elektronische Ressource
|c by Jeff M. Phillips
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| 250 |
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|a 1st ed. 2021
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| 260 |
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|a Cham
|b Springer International Publishing
|c 2021, 2021
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| 300 |
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|a XVII, 287 p. 109 illus., 108 illus. in color
|b online resource
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| 505 |
0 |
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|a Probability review -- Convergence and sampling -- Linear algebra review -- Distances and nearest neighbors -- Linear Regression -- Gradient descent -- Dimensionality reduction -- Clustering -- Classification -- Graph structured data -- Big data and sketching
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| 653 |
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|a Computational Mathematics and Numerical Analysis
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| 653 |
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|a Mathematics / Data processing
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| 653 |
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|a Information visualization
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| 653 |
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|a Data and Information Visualization
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| 041 |
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7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b Springer
|a Springer eBooks 2005-
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| 490 |
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|a Springer Series in the Data Sciences
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| 028 |
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|a 10.1007/978-3-030-62341-8
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| 856 |
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|u https://doi.org/10.1007/978-3-030-62341-8?nosfx=y
|x Verlag
|3 Volltext
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| 082 |
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|a 518
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| 520 |
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|a This textbook, suitable for an early undergraduate up to a graduate course, provides an overview of many basic principles and techniques needed for modern data analysis. In particular, this book was designed and written as preparation for students planning to take rigorous Machine Learning and Data Mining courses. It introduces key conceptual tools necessary for data analysis, including concentration of measure and PAC bounds, cross validation, gradient descent, and principal component analysis. It also surveys basic techniques in supervised (regression and classification) and unsupervised learning (dimensionality reduction and clustering) through an accessible, simplified presentation. Students are recommended to have some background in calculus, probability, and linear algebra. Some familiarity with programming and algorithms is useful to understand advanced topics on computational techniques
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