Wednesday, July 18 Lecture: 9:00-10:15 Break: 10:15-10:45 Lecture: 10:45-12:00
Monday’s morning: Supervised Learning
Linear discriminant analysis (LDA),
Support vector machines (SVMs),
K-nearest neighbors (k-NN),
Classification trees (CT)
Monday’s afternoon: Model Evaluation and Feature Selection
Model evaluation: Confusion matrix, loss function, hypothesis testing,
Feature selection: Principal component analysis (PCA), ROC, hypothesis testing
Unsupervised learning: k-means clustering,
Neural networks and deep learning
3 Level set method and mean curvature flow equation
Instructor: Hung Tran (University of Wisconsin Madison, Mỹ).
Abstract: I will present some basic results on the level set method and mean curvature flow equation (MCF). In particular, I will prove well-posedness of viscosity solutions to MCF. Some background on viscosity solutions can be found in Appendix of the lecture notes of Mitake and I http://www.math.wisc.edu/~hung/Mitake-Tran-LN.pdf, but are not really required to take the class.