Mathematical foundations for data analysis
Material type:
TextSeries: Springer Series in the Data SciencesPublication details: Switzerland : Springer, 2021 Description: xvii, 287pISBN: 9783030623401Subject(s): Data mining -- Mathematics | Machine learning -- Mathematics | Data analysis | Neural networks | Data sciences | Dimensionality reduction | Big dataDDC classification: 004.8 Online resources: Table of contents | Reviews Summary: 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.
| Item type | Current library | Call number | Status | Date due | Barcode |
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Book
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NISER LIBRARY | 004.8 PHI-M (Browse shelf(Opens below)) | Available | 25324 |
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| 004.8 LIU-P Python machine learning by example : | 004.8 MUL-I Introduction to Machine Learning with Python. | 004.8 NEA-A Artificial intelligence: with an introduction to machine learning | 004.8 PHI-M Mathematical foundations for data analysis | 004.8 POO-A Artificial Intelligence | 004.8 RUS-A Artificial Intelligence:a modern approach | 004.8 RUS-A Artificial Intelligence:a modern approach |
Includes bibliographical references and index
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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