Doctoral students, advanced master’s students, and researchers in mathematics, statistics, computer science, electrical engineering, and related fields who want a deeper understanding of probabilistic methods in modern data science. Suitable for self-study or a second probability course.
Modern data science relies heavily on probability. Traditional graduate courses often do not reach the level needed for contemporary research. This book bridges that gap.
Master’s-level probability, strong linear algebra, and familiarity with normed and Hilbert spaces. Measure theory is not required.