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Amazon | Mathematical Methods in Data Science | Ren, Wang | Machine Learning

Source: https://www.amazon.co.jp/dp/0443186790?tag=jplinktagdefault-22&geniuslink=true

レビュー

“This book is an interesting introduction to mathematical methods for data science. It covers ordinary differential equations and partial differential equations, and this is a main feature that distinguishes the book from others. The first chapters start gently to build some mathematical background on linear algebra, probability, calculus, and optimization. In the fourth chapter, the book presents real-world use of these mathematical tools for network analysis. Then the book goes deeper into the subject and discusses the methodologies of ordinary differential equations and partial differential equations, as well as their applications. Overall, the book is suitable for advanced undergraduate and beginning graduate students interested in mathematical data science methods.”--Liangzu Peng, zbMATHOpen

著者について

She received the Ph.D. degree in applied mathematics from Beijing Institute of Technology, Beijing, China, in 2004. Her research interests include data science, applied mathematics, and applied statistics. She conducted five Projects of National Nature Science Foundation of China, one Alexander von Humboldt Fellowship for Experienced Researcher, and five Provincial Projects. She has published numerous articles in scholarly journals, such as Acta Mater.、Appl. Phys. Lett.、IEEE Trans. SMC、Infor. Sci.、J. Stat. Phys.、J. Nonlinear Sci.、 Phys. Rev. B、Phys. Rev. E、Sci. China Math.、Sci. China Phys. and Sci. China Mater., etc.

He completed his doctorate in mathematics, while also earning a master’s degree in computer science at Michigan State University in 1997. He worked as a full-time software engineer in industry for almost ten years before joining Arizona State University. Dr. Wang’s research interests include applied mathematics, data science, differential equations, online social networks. He has published numerous articles in scholarly journals and a book entitled, “Modeling Information Diffusion in Online Social Networks with Partial Differential Equations”, Springer, 2020. Recently he developed and taught a course, Mathematical Methods in Data Science, at Arizona State University.