The emphasis of the course is on models for systems with uncertainty. We study the properties of various models and discuss how they can be applied to analyze algorithm and system performance. This course is useful for students interested in the analysis of algorithms and systems with uncertainty that arise in computer systems, reinforcement learning, social networks, online markets, etc. Concepts covered include renewal processes, Markov chains and processes, and Markov decision processes.