ECE 228. Machine Learning for Physical Applications (4 units)
Link to catalog page: https://catalog.ucsd.edu/courses/ECE.html#ece228
Description
Machine learning has received enormous interest. To learn from data we use probability theory, which has been a mainstay of statistics and engineering for centuries. The class will focus on implementations for physical problems. Topics: Gaussian probabilities, linear models for regression, linear models for classification, neural networks, kernel methods, support vector machines, graphical models, mixture models, sampling methods, and sequential estimation. Prerequisites: graduate standing.
Prerequisite courses
ECE 228 has no prerequisite courses.
Successor courses
No courses have ECE 228 as a prerequisite.