ENME618
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Uncertainty Quantification and Scientific Machine Learning
Subject
ENME - Mechanical Engineering
Description
An introduction to predictive modeling and uncertainty quantification for scientific and engineering applications. Key concepts include: Bayesian interference, stochastic differential equations, Monte Carlo methods for uncertainty propagation; supervised learning techniques including Bayesian regression, Gaussian processes, and deep learning; unsupervised methods; state space models with applications such as mechanical vibrations. A hands-on approach to data science and machine learning is emphasized over theoretical derivations.
Signature Learning
Research & Creative Scholarship
Course Attributes
Fee Rate Group(Domestic) - B, Fee Rate Group(International) -D, GFC Hours (3-0), RCS Related, Research & Creative Scholarship - Related
Courses may consist of a Lecture, Lab, Tutorial, and/or Seminar. Students will be required to register in each component that is required for the course as indicated in the schedule of classes. Practicums, internships or other experiential learning modalities are typically indicated as a Lab component.
Component
LEC
Units
3
Repeat for Credit
No
Subject code
ENME