1 Bayesian methods for machine learning

• Instructor: Lizhen Lin (University of Notre Dame, Mỹ)
• Time: Thứ năm, Thứ sáu 5–6/7, 2 buổi sáng, 9:00-11:30, Phòng F207.
• Abstract:
  1. Parametric Bayesian analysis in Machine learning
     • Basics on Bayesian inference;
     • An intro to Markov Chain Monte Carlo (e.g., Gibbs sampler);
     • Factor analysis and dictionary learning;
  2. Non-parametric Bayesian analysis in Machine learning
     • Dirichlet process (DP);
     • DP mixture models;
     • Gaussian process for regression/classification/density estimation; Latent gaussian process models;
     • Bayesian optimizations;
  3. Neural networks and Bayesian deep learning (if time permits);

2 An Introduction to the mathematics of machine learning

• Instructor: Hien Tran (North Carolina State University Raleigh, Mỹ)
• Time: 16–18/7, 3 buổi, Phòng F207
• Monday, July 16
  Lecture:     9:00-10:15
  Break:    10:15-10:45
  Lecture:    10:45-12:00
  
  Lecture:    2:00-3:15
  Break:    3:15-3:45
  Lecture:    3:45-5:00
  
  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.
• Time: 16–19/7, 4 buổi, Phòng F207.