Listed in: Computer Science, as COSC-223
Kristen S. Gardner (Section 01)
Probability is everywhere in computer science. In networks and systems, it is a key tool that allows us to predict performance, to understand how delay changes with the system parameters, and more. In algorithms, randomization is used to design faster and simpler algorithms than their deterministic counterparts. In machine learning, probability is central to the underlying theory. This course provides an introduction to probability with a focus on computer science applications. We will discuss elementary probability theory, including topics such as discrete and continuous random variables and distributions and Markov chains, and settings in which these are used in computer science (e.g., modeling real-world workload distributions, analyzing computer system performance, and designing and analyzing randomized algorithms).
Requisite: MATH 111 and COSC 112/211. Spring semester. Assistant Professor Gardner.