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Subject offerings of new and developing areas of knowledge in computer science intended to augment the existing curriculum. See Class Schedule or departmental course information for topics and prerequisites. Course Information: May be repeated in the same or separate terms if topics vary.

This course is an introduction to causal inference and bayesian statistics. The course will cover the following topics: (1) causal inference, including the structural causal models, directed acyclic graphs, counterfactuals, the do-calculus, the backdoor criterion, the front-door criterion, the instrumental variable approach, the propensity score matching method, and the potential outcomes framework; (2) Bayesian estimates of parameters, including Markov chain Monte Carlo and stochastic variational inference methods (3) Applications of causal inference and Bayesian statistics in HCI, especially with empirical data. The course will include lectures, discussions, and hands-on programming assignments using Python. Students will be expected to complete a final project that applies the concepts learned in the course to a real-world problem. This section is intended for Chicago MCS only. There may be online and in person components. You are responsible for completing homeworks, quizzes, and