This seminar explores parallels between human cognition and ideas in probability and statistics, with an emphasis on statistical machine learning. Minds and machines face similar computational problems, meaning that we can develop new hypotheses about human cognition by seeing how those problems are solved in statistics and find new challenges for machine learning by studying human cognition. Topics will include causal learning, clustering, Markov chain Monte Carlo, function learning, and randomness. Students will complete an independent research project related to computational modeling of human cognition.
Prerequisites are Psych 123/Cogsci 131 or Computer Science 188 or 281A, or an equivalent familiarity with ideas from statistics and machine learning.