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M X93C M 393C  Topics in Applied Mathematics  3 Hours

3 Lecture Hours  0 Lab Hours  
Recent topics have included quantum mechanics, statistical physics, ergodic theory, group representations, statistical mechanics, quantum field theory, introductory partial differential equations, monotone operators and partial differential equations, Hilbert space methods for partial differential equations, Hamiltonian dynamics, nonlinear functional analysis, Euler and Navier-Stokes equations, microlocal calculus and spectral asymptotics, calculus of variations. Three lecture hours a week for one semester.
Pre/Corequisites: Graduate standing and consent of instructor.  
Grading: Student Option  
Repeatable for credit: May be repeated for credit when the topics vary.  
Academic Level: Doctoral  
Topic 1: Neural Dynamics, Information Theory, and Machine Learning.
  

M X93C.1 M 393C.1  Neural Dynamics, Information Theory, and Machine Learning  3 Hours

3 Lecture Hours  0 Lab Hours  
Grading: Student Option  
Repeatable for credit: No  
Academic Level: Doctoral