An Introduction with Applications in Data Science
This textbook is aimed at doctoral students, advanced master's students, and beginning researchers in mathematics, statistics, computer science, electrical engineering, and related fields, who seek to deepen their understanding of probabilistic methods commonly used in modern data science research. It can be used for self-study or as a textbook for a second probability course with data science applications.
Data science is evolving rapidly, and probabilistic methods are key to these advances. A typical graduate probability course no longer provides the mathematical sophistication needed for early-career data science researchers. This book aims to fill that gap, presenting essential probabilistic methods and results for mathematical data scientists.
To read this book, you will need a solid knowledge of probability theory (at the masters or doctoral level), strong undergraduate linear algebra, and some familiarity with metric, normed, and Hilbert spaces. Measure theory is not required.
NEW! The draft of the second edition is now online:
Want to get notified when the printed version is out? Email me: rvershyn@uci.edu
The first eidition in printed form can be purchased on Amazon and in Cambridge University Press. It is freely available online: