Probability review.- Convergence and sampling.- Linear algebra review.- Distances and nearest neighbors.- Linear Regression.- Gradient descent.- Dimensionality reduction.- Clustering.- Classification.- Graph structured data.- Big data and sketching.
Jeff M. Phillips is an Associate Professor in the School of Computing within the University of Utah. He directs the Utah Center for Data Science as well as the Data Science curriculum within the School of Computing. His research is on algorithms for big data analytics, a domain with spans machine learning, computational geometry, data mining, algorithms, and databases, and his work regularly appears in top venues in each of these fields. He focuses on a geometric interpretation of problems, striving for simple, geometric, and intuitive techniques with provable guarantees and solve important challenges in data science. His research is supported by numerous NSF awards including an NSF Career Award.
“This is certainly a timely book with large potential impact and
appeal. … the book is therewith accessible to a broad scientific
audience including undergraduate students. … Mathematical
Foundations for Data Analysis provides a comprehensive exploration
of the mathematics relevant to modern data science topics, with a
target audience that is looking for an intuitive and accessible
presentation rather than a deep dive into mathematical
intricacies.” (Aretha L. Teckentrup, SIAM Review, Vol. 65 (1),
March, 2023)
“The book is fairly compact, but a lot of information is presented
in those pages. … the book is pretty much self-contained, but prior
knowledge of linear algebra and python programming would benefit
anyone. The clear writing is backed in many instances by helpful
illustrations. Color is used judiciously throughout the text to
help differentiate between objects and highlight items of interest.
… Phillips’ book is much more concise, but still discusses many
different mathematical aspects of data science.” (David R. Gurney,
MAA Reviews, September 5, 2021)
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