Wednesday, July 18 Lecture: 9:00-10:15 Break: 10:15-10:45 Lecture: 10:45-12:00

Abstract:

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

Wednesday’s morning:

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.