Mathematical Methods In Data Science
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Data Science and Machine Learning
Author | : Dirk P. Kroese,Zdravko Botev,Thomas Taimre,Radislav Vaisman |
Publsiher | : CRC Press |
Total Pages | : 538 |
Release | : 2019-11-20 |
Genre | : Business & Economics |
ISBN | : 9781000730777 |
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Focuses on mathematical understanding Presentation is self-contained, accessible, and comprehensive Full color throughout Extensive list of exercises and worked-out examples Many concrete algorithms with actual code
Mathematical Problems in Data Science
Author | : Li M. Chen,Zhixun Su,Bo Jiang |
Publsiher | : Springer |
Total Pages | : 213 |
Release | : 2015-12-15 |
Genre | : Computers |
ISBN | : 9783319251271 |
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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 of big data, geometric data structures, topological data processing, and various learning methods. For unsolved problems such as incomplete data relation and reconstruction, the book includes possible solutions and both statistical and computational methods for data analysis. Initial chapters focus on exploring the properties of incomplete data sets and partial-connectedness among data points or data sets. Discussions also cover the completion problem of Netflix matrix; machine learning method on massive data sets; image segmentation and video search. This book introduces software tools for data science and Big Data such MapReduce, Hadoop, and Spark. This book contains three parts. The first part explores the fundamental tools of data science. It includes basic graph theoretical methods, statistical and AI methods for massive data sets. In second part, chapters focus on the procedural treatment of data science problems including machine learning methods, mathematical image and video processing, topological data analysis, and statistical methods. The final section provides case studies on special topics in variational learning, manifold learning, business and financial data rec overy, geometric search, and computing models. Mathematical Problems in Data Science is a valuable resource for researchers and professionals working in data science, information systems and networks. Advanced-level students studying computer science, electrical engineering and mathematics will also find the content helpful.
The Mathematics of Data
Author | : Michael W. Mahoney,John C. Duchi,Anna C. Gilbert |
Publsiher | : American Mathematical Soc. |
Total Pages | : 325 |
Release | : 2018-11-15 |
Genre | : Big data |
ISBN | : 9781470435752 |
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Probability and Statistics for Data Science
Author | : Norman Matloff |
Publsiher | : CRC Press |
Total Pages | : 295 |
Release | : 2019-06-21 |
Genre | : Business & Economics |
ISBN | : 9780429687112 |
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Probability and Statistics for Data Science: Math + R + Data covers "math stat"—distributions, expected value, estimation etc.—but takes the phrase "Data Science" in the title quite seriously: * Real datasets are used extensively. * All data analysis is supported by R coding. * Includes many Data Science applications, such as PCA, mixture distributions, random graph models, Hidden Markov models, linear and logistic regression, and neural networks. * Leads the student to think critically about the "how" and "why" of statistics, and to "see the big picture." * Not "theorem/proof"-oriented, but concepts and models are stated in a mathematically precise manner. Prerequisites are calculus, some matrix algebra, and some experience in programming. Norman Matloff is a professor of computer science at the University of California, Davis, and was formerly a statistics professor there. He is on the editorial boards of the Journal of Statistical Software and The R Journal. His book Statistical Regression and Classification: From Linear Models to Machine Learning was the recipient of the Ziegel Award for the best book reviewed in Technometrics in 2017. He is a recipient of his university's Distinguished Teaching Award.
Mathematical Methods in Data Science
Author | : Jingli Ren,Haiyan Wang |
Publsiher | : Elsevier |
Total Pages | : 260 |
Release | : 2023-01-06 |
Genre | : Computers |
ISBN | : 9780443186806 |
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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 authors’ recently published and previously unpublished results, this book introduces a new approach based on network analysis to integrate big data into the framework of ordinary and partial differential equations for dataanalysis and prediction. With data science being used in virtually every aspect of our society, the book includes examples and problems arising in data science and the clear explanation of advanced mathematical concepts, especially data-driven differential equations, making it accessible to researchers and graduate students in mathematics and data science. Combines a broad spectrum of mathematics, including linear algebra, optimization, network analysis and ordinary and partial differential equations for data science Written by two researchers who are actively applying mathematical and statistical methods as well as ODE and PDE for data analysis and prediction Highly interdisciplinary, with content spanning mathematics, data science, social media analysis, network science, financial markets, and more Presents a wide spectrum of topics in a logical order, including probability, linear algebra, calculus and optimization, networks, ordinary differential and partial differential equations
Mathematics for Machine Learning
Author | : Marc Peter Deisenroth,A. Aldo Faisal,Cheng Soon Ong |
Publsiher | : Cambridge University Press |
Total Pages | : 391 |
Release | : 2020-04-23 |
Genre | : Computers |
ISBN | : 9781108470049 |
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Distills key concepts from linear algebra, geometry, matrices, calculus, optimization, probability and statistics that are used in machine learning.
High Dimensional Probability
Author | : Roman Vershynin |
Publsiher | : Cambridge University Press |
Total Pages | : 299 |
Release | : 2018-09-27 |
Genre | : Business & Economics |
ISBN | : 9781108415194 |
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An integrated package of powerful probabilistic tools and key applications in modern mathematical data science.
Mathematical Foundations of Data Science Using R
Author | : Frank Emmert-Streib,Salissou Moutari,Matthias Dehmer |
Publsiher | : Walter de Gruyter GmbH & Co KG |
Total Pages | : 444 |
Release | : 2022-10-24 |
Genre | : Computers |
ISBN | : 9783110796179 |
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The aim of the book is to help students become data scientists. Since this requires a series of courses over a considerable period of time, the book intends to accompany students from the beginning to an advanced understanding of the knowledge and skills that define a modern data scientist. The book presents a comprehensive overview of the mathematical foundations of the programming language R and of its applications to data science.