Applied Math Collective
The Applied Math Collective is a graduate-student-led seminar, where the aim is to provide an informal platform where the speaker discusses general interest “SIAM review”-style applied math papers. We meet Thursdays at 4pm in LCB 222, when the Department Colloquium does not have a speaker. This seminar welcomes all students (graduate or undergraduate), postdocs and faculty and has been running since the Fall 2016, thanks the initiative of Christel Hohenegger and Braxton Osting.
If you are interested in giving a talk or simply getting into the mailing list, please send an email to Nathan Willis (willis at math dot utah dot edu)
Fall 2019 talks (LCB 222 unless specified otherwise)
Thu Aug 29 2019, Title: Organizational meeting
Thu Sep 5 2019 (department colloquium)
Thu Sep 12 2019 Hyunjoong Kim, Nathan Willis, and RK Yoon
Talk about summer workshop experiences
Thu Sep 19 2019 (department colloquium)
Thu Sep 26 2019, Elias Clark, Title: The German Tank Problem
Abstract: The German Tank Problem is a classic example of the application of statistical analysis to economic intelligence. During the Second World War, German tanks were marked with sequential serial numbers. By analyzing captured and destroyed tanks, the USA and UK were able to make surprisingly accurate estimates of German tank production. This talk will discuss how these estimates were made, and other applications of serial number analysis.
Thu Oct 3 2019, RK Yoon, Title: Introduction to reinforcement learning
Abstract: In these days, the reinforcement learning (RL) is widely studied in many. The most popular example showing the power of reinforcement learning is the fact that the AlphaGo, an AI-powered system, beats the champion of the complex boardgame Go by 4 points to 1.
Unlike other machine learning algorithms relying on complete models or exemplary supervision, reinforcement learning is focused on the goal-directed learning from the interaction between the agent and its environment like states, actions and rewards.