<p>This book describes current problems in data science and Big Data. Key topics are data classification, Graph Cut, the Laplacian Matrix, Google Page Rank, efficient algorithms, hardness of problems, different types ofΒ big data, geometric data structures, topological data processing, and various le
Information Theoretic Methods in Data Science
- Year
- 2021
- Tongue
- English
- Leaves
- 561
- Category
- Library
No coin nor oath required. For personal study only.
π SIMILAR VOLUMES
<p>This book describes current problems in data science and Big Data. Key topics are data classification, Graph Cut, the Laplacian Matrix, Google Page Rank, efficient algorithms, hardness of problems, different types ofΒ big data, geometric data structures, topological data processing, and various le
<p>This book describes current problems in data science and Big Data. Key topics are data classification, Graph Cut, the Laplacian Matrix, Google Page Rank, efficient algorithms, hardness of problems, different types ofΒ big data, geometric data structures, topological data processing, and various le
In this mathematical autobiography, Gregory Chaitin presents a technical survey of his work and a non-technical discussion of its significance. The volume is a companion to the earlier collection of Chaitin's papers "Information, Randomness and Incompleteness" also published by World Scientific. The
<p><span>Mathematical Methods in Data Science</span><span> introduces a new approach based on network analysis to integrate big data into the framework of ordinary and partial differential equations for data analysis and prediction. The mathematics is accompanied with examples and problems arising i
Mathematical Methods in Data Science covers a broad range of mathematical tools used in data science, including calculus, linear algebra, optimization, network analysis, probability and differential equations. Based on the authorsβ recently published and previously unpublished results, this book int