Instructor: Fei Lu
Class meets: TTh, 10:30-11:45, Shriver Hall 104
Office Hours: TTh 9:30--10:30, Krieger 301
Webpage:
http://www.math.jhu.edu/~feilu/20Spring/StoDS/stoDS.html
Email:
feilu## ( ## = @math.jhu.edu)
This topic course will run in a reading/discussion/projects fashion. References are attached to each topic. In addition, the following books may be helpful (To be updated).
Course plan (tentative): This topic course will explore systems of interacting particles or agents: their dynamics, inference, and applications in machine learning. For the study of the dynamics, topics include stability and ergodicity of the systems, including first-order gradient systems, 2nd-order Hamiltonian systems and related mean field equations and propagation of chaos. For the applications, topics include optimization algorithms such as stochastic gradient decent (SGD) and particle SGD, and sampling methods using particle systems such as Stein variational gradient decent. For the inference, we will consider the estimation of the interaction kernels as well as state estimation using data assimilation techniques. If time permits applications in network and control with be explored. This topic course will run in a reading/discussion/projects fashion.
Prerequisite: probability; differential equations (preferably stochastic differential equations).
Grading: Grade will be based on project assignments and presentations. There is no exam.
Tentative schedule (will be updated weekly):