Computer Science (CS) Graduate Courses (2017-18)
HPS/Pl/CS 110. Causation and Explanation. 9 units (3-0-6): first term. An examination of theories of causation and explanation in philosophy and neighboring disciplines. Topics discussed may include probabilistic and counterfactual treatments of causation, the role of statistical evidence and experimentation in causal inference, and the deductive-nomological model of explanation. The treatment of these topics by important figures from the history of philosophy such as Aristotle, Descartes, and Hume may also be considered. Instructor: Eberhardt.
Ec/ACM/CS 112. Bayesian Statistics. 9 units (3-0-6): third term. This course provides an introduction to Bayesian Statistics and its applications to data analysis in various fields. Topics include: discrete models, regression models, hierarchical models, model comparison, and MCMC methods. The course combines an introduction to basic theory with a hands-on emphasis on learning how to use these methods in practice so that students can apply them in their own work. Previous familiarity with frequentist statistics is useful but not required. Instructor: Rangel.
CS/SS/Ec 149. Algorithmic Economics. 9 units (3-0-6): second term. This course will equip students to engage with active research at the intersection of social and information sciences, including: algorithmic game theory and mechanism design; auctions; matching markets; and learning in games. Instructor: Echenique/Pomatto.
SS/CS 241. Topics in Algorithmic Economics. 9 units (3-0-6): . This is a graduate-level seminar covering recent topics at the intersection of computer science and economics. Topics will vary, but may include, e.g., dynamics in games, algorithmic mechanism design, and prediction markets. Not offered 2017-18. Instructor: EAS and HSS faculty.