An Introduction with Applications in Data Science
This is a textbook in probability in high dimensions with a view toward applications in data sciences. It is intended for doctoral and advanced masters students and beginning researchers in mathematics, statistics, electrical engineering, computer science, computational biology and related areas, who are looking to expand their knowledge of theoretical methods used in modern research in data sciences.
Data sciences are moving fast, and probabilistic methods often provide a foundation and inspiration for such advances. A typical graduate probability course is no longer sufficient to acquire the level of mathematical sophistication that is expected from a beginning researcher in data sciences today. The proposed book intends to partially cover this gap. It presents some of the key probabilistic methods and results that should form an essential toolbox for a mathematical data scientist. This book can be used as a textbook for a basic second course in probability with a view toward data science applications. It is also suitable for self-study.
The essential prerequisites for reading this book are a rigorous course in probability theory (on Masters or Ph.D. level), an excellent command of undergraduate linear algebra, and general familiarity with basic notions about Hilbert and normed spaces and linear operators. Knowledge of measure theory is not essential but would be helpful.
Download the final draft of the book for free:
Use this draft at your own risk, and only for your personal and classroom needs. Please do not distribute the copy.