printer friendly pageCME 108: Introduction to scientific computing with machine learning applications
Numerical computation for engineering and machine learning applications: error analysis, floating-point arithmetic, numerical solution of linear and nonlinear equations, optimization, gradient descent, polynomial interpolation, numerical differentiation and integration, supervised learning, numerical solution of ordinary differential equations, numerical stability, unsupervised learning, sampling (Monte Carlo algorithms). Implementation of numerical methods in programming assignments (Python or Matlab). Prerequisites:
CME 100, 102 or
MATH 51, 52, 53; prior programming experience (MATLAB or other language at level of
CS 106A or higher).
Terms: Aut
| Units: 3
| UG Reqs: WAY-AQR, WAY-FR, GER:DB-EngrAppSci
Instructors:
Ying, L. (PI)
;
Chen, H. (TA)
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