SE 232. Machine Learning in Computational Mechanics (4 units)
Link to catalog page: https://catalog.ucsd.edu/courses/SE.html#se232
Description
Provides background and tools to apply machine learning to solve problems in computational mechanics and engineering. An overview of the basic principles of machine learning will be provided, including supervised and unsupervised learning, regression, classification, and generative algorithms versus discriminative algorithms. Focus will be given to deep neural networks, convolutional neural networks, recurrent neural networks, physics-informed machine learning and implementation in Python. Recommended preparation: knowledge of computer programming, probability theory, linear algebra, and solid mechanics. Prerequisites: graduate standing or consent of instructor.
Prerequisite courses
SE 232 has no prerequisite courses.
Successor courses
No courses have SE 232 as a prerequisite.