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Probability and Statistics for Machine Learning

A Textbook
BuchGebunden
522 Seiten
Englisch
Springererschienen am15.05.20242024
This book covers probability and statistics from the machine learning perspective. From probability to machine learning: Many machine learning applications are addressed using probabilistic models, whose parameters are then learned in a data-driven manner.mehr
Verfügbare Formate
BuchGebunden
EUR96,29
E-BookPDF1 - PDF WatermarkE-Book
EUR96,29

Produkt

KlappentextThis book covers probability and statistics from the machine learning perspective. From probability to machine learning: Many machine learning applications are addressed using probabilistic models, whose parameters are then learned in a data-driven manner.
Details
ISBN/GTIN978-3-031-53281-8
ProduktartBuch
EinbandartGebunden
Verlag
Erscheinungsjahr2024
Erscheinungsdatum15.05.2024
Auflage2024
Seiten522 Seiten
SpracheEnglisch
Gewicht1130 g
IllustrationenXVIII, 522 p. 1 illus.
Artikel-Nr.55812790

Inhalt/Kritik

Inhaltsverzeichnis
Chapter. 1. Probability and Statistics: An Introduction.- Chapter. 2. Summarizing and Visualizing Data.- Chapter. 3. Probability Basics and Random Variables.- Chapter. 4. Probability Distributions.- Chapter. 5. Hypothesis Testing and Confidence Intervals.- Chapter. 6. Reconstructing Probability Distributions from Data.- Chapter. 7. Regression.- Chapter. 8. Classification: A Probabilistic View.- Chapter. 9. Unsupervised Learning: A Probabilistic View.- Chapter. 10. Discrete State Markov Processes.- Chapter. 11. Probabilistic Inequalities and Extreme Value Analysis.- Bibliography.- Index.mehr

Autor

Charu C. Aggarwal is a Distinguished Research Staff Member (DRSM) at the IBM T. J. Watson Research Center in Yorktown Heights, New York. He completed his undergraduate degree in Computer Science from the Indian Institute of Technology at Kanpur in 1993 and his Ph.D. in Operations Research from the Massachusetts Institute of Technology in 1996. He has published more than 400 papers in refereed conferences and journals, and has applied for or been granted more than 80 patents. He is author or editor of 20 books, including textbooks on linear algebra, machine learning, neural networks, and outlier analysis. Because of the commercial value of his patents, he has thrice been designated a Master Inventor at IBM. He has received several awards, including the EDBT Test-of-Time Award (2014), the ACM SIGKDD Innovation Award (2019), the IEEE ICDM Research Contributions Award (2015), and the IIT Kanpur Distinguished Alumnus Award (2023).He is also a recipient of the W. Wallace McDowell Award, the highest award given solely by the IEEE Computer Society across the field of computer science. He has served as an editor-in-chief of ACM Books and is currently serving as an editor-in-chief of the ACM Transactions on Knowledge Discovery from Data. He is a fellow of the SIAM, ACM, and the IEEE, for"contributions to knowledge discovery and data mining algorithms."