Slides, notes, demos, and videos for lectures will be posted here.
Topics may change based on student interest.
Lectures by date: topics and videos
Course topics: slides, demos, and other resources
Introduction
Linear models
Generalization and overfitting
Midterm exam
Loss functions for messy labels
Regularization for messy features
Unsupervised learning
Missing data and PCA
Sparse PCA, NNMF, kmeans
Optimization: AM and PALM
Nonlinear models
Learning from prototypes
Nearest neighbors
Smoothing
Graphs and networks
Trustworthy machine learning
Other resources
Review/overview
Julia
GitHub
