Second Edition
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 a deeper understanding of probabilistic methods used in modern data science research. It can be used for self-study or as a textbook for a second probability course.
Data science evolves rapidly, and probabilistic methods are central to these advances. A traditional graduate probability course often does not provide the mathematical sophistication required for modern research in data science. This book fills that gap.
You should know probability theory at the master's or doctoral level, strong undergraduate linear algebra, and basic metric, normed, and Hilbert spaces. Measure theory is not required.
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