Andrea Bertozzi
Da Kuang

Course description:
This is a graduate-level seminar course that introduces advanced machine learning methods. Topics and relevant papers are listed below. You will work on cutting-edge research problems and write course reports that can potentially lead to publications. You are expected to read selected papers and write programs for numerical experiments, where the choice of programming language will depend on your project topic. At the end of this course, you will present your project to the class.


Date Mon Wed Fri
Mar 28
Mar 30
Apr 1
Course introduction
Data sets
Point process models Machine learning overview
Apr 4
Apr 6
Apr 8
PCA K-means Spectral clustering
Apr 11
Apr 13
Apr 15
Nonnegative matrix factorization Nonnegative matrix factorization Latent Dirichlet allocation
Apr 18
Apr 20
Apr 22
Review of diffuse interface PDE Basics of GL and MBO Theorems for Gamma convergence
Apr 25
Apr 27
Apr 29
Theorems for convergence stability
of graph GL
Theorems on mean curvature
Global/Local minimizers
May 2
May 4
May 6
Modularity optimization Modularity optimization Parallel methods
May 9
May 11
May 13
Computer architectre overview Computer architecture overview Numerical software stacks
May 16
May 18
May 20
Visualization Matrix completion Convolutional neural networks
May 23
May 25
May 27
Project presentation Project presentation Project presentation
May 30
Jun 1
Jun 3
(Memorial Day holiday) Project presentation Project presentation
Jun 6

(No final exam)

Paper lists (constantly updated)