Machine Learning for Physical Applications

4 credits

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.