Meeting Times:  Mondays and Wednesdays, 3:00-4:15
Location:  ES&T L1125

Instructor:  Edmond Chow
E-mail: 
Office Hours:  By appointment


Course Description

This course will cover many of the major computational methods used in science and engineering for numerical simulation, inverse problems, and machine learning. Students will deepen their understanding of these methods through programming and experimentation. Examples will be given in Matlab.

Topics

  • Finite element method
  • Fast multipole method
  • Bayesian estimation theory
  • Gaussian processes and kernel methods
  • Numerical optimization
  • Electronic structure calculations
  • Brownian dynamics simulation
Prerequisites

Graduate-level Numerical Linear Algebra (CSE/MATH 6643 and/or CSE/MATH 6644). Experience with Matlab.

Grading

60% Assignments
30% Final project (in-class presentation, final report)
10% Class participation

Textbook

There is no textbook for this course. References for resources will be given in class.