All students are strongly encouraged to engage deeply in the course, interact with others, and actively participate (in person during class and asynchronously out of class) in all HS650 activities. Attending lectures regularly in person is important -- physical presence may be the most effective learning strategy. Lectures may be streamed and recorded only under special (uncommon) circumstances. Not all lectures will be recorded.

Mon Sept 11 - End of Chapter 1 (Intro) + Start of Chapter 2 (EDA/Viz)
Wed Sept 13 - Chapter 2 Imputation and Rebalancing (Part 1) + Chapter 2 Rebalancing (Part 2)
Mon Sept 18 - Chapter 2 (Viz)
Wed Sept 20 - End of Chapter 2 (Viz) and start of Chapter 3 (Linear Algebra)
Mon Sept 25 - Chapter 3 (Linear Models)
Wed Sept 27 - Chapter 4 Linear Dimensionality Reduction
Mon Oct 2 - Chapter 4 (nonlinear dimensionality reduction, t-SNE, UMAP)
Wed Oct 4 - Chapter 5 (Supervised ML)
Mon Oct 9 - Chapter 5 (Supervised Classification)
Wed Oct 11 - Chapter 6 (SVM)
Wed Oct 18 - Chapter 7 (NLM, TM, Apriori Rules)
Mon Oct 23 - Chapter 8 (Unsupervised Clustering)
Wed Oct 25 - Chapter 9 (Model Performance Eval + CV)
Mon Oct 30 - Chapter 10 - Specialized Machine Learning Topics
Wed Nov 1 - Chapter 11 - Feature/Variable Selection
Mon Nov 6 - Chapter 11 - Feature/Variable Selection & Chapter 12 (Big Longitudinal Data/ARIMA time-series modeling)
Wed Nov 8 - Chapter 12 (Big Longitudinal Data)
Mon Nov 13 - Chapter 13 (Optimization, Part 1)
Wed Nov 15 - Chapter 13 (Optimization, Part 2 and Part 3)
Mon Nov 20 - Chapter 13 (Optimization, Part 4) and Chapter 14 (Deep Learning, Part 1)
Mon Nov 27 - Chapter 14 (Part 2)
Wed Nov 29 - Chapter 14 (Part 3)
...