Introduction to Probability for Computing

Introduction to Probability for Computing by Mor Harchol-Balter serves as a specialized pedagogical resource tailored to the intersection of probability theory and computer science. The book addresses the necessity for computer scientists to master probabilistic reasoning, which is essential for analyzing randomized algorithms, network performance, and machine learning models. The scope of the work is comprehensive, covering fundamental concepts such as sample spaces, random variables, and expectation, while extending into more advanced topics relevant to computational theory. A notable characteristic of this book is its deliberate focus on intuition and application rather than purely abstract mathematical derivation. By grounding theoretical concepts in examples drawn from computer science, the author facilitates a more direct connection between mathematical principles and their practical utility in software and systems design. The organization of the material is structured to support a semester-long academic course, providing a logical progression from basic axioms to complex probabilistic analysis. The book is particularly well-suited for undergraduate students who possess a background in discrete mathematics but require a more rigorous treatment of probability that aligns with their specific field of study. The pedagogical approach emphasizes clarity and the development of problem-solving skills, which are critical for students navigating the complexities of modern computational systems. From an Amerbach perspective, this book occupies a significant position in the academic literature by bridging the gap between traditional probability textbooks—which often lean heavily toward statistics or pure mathematics—and the specific needs of the computing discipline. It provides a robust framework for students to understand how probabilistic models function within the constraints and requirements of computer science, making it a valuable addition to the undergraduate curriculum.