Textbook: An Introduction to Machine Learning (ISLR) (free PDF download)
Week 1: ML Overview
Week 3: Regression 2: feature reduction, expansion & engineering
Week 4: Tree-based methods 1: tree, bagging, random forest
Week 5: Tree-based methods 2: boosting & xgboost
No Meeting on 6/29
Week 6: Review
Week 7: K-Nearest Neighbors; Unsupervised learning: K-Means Clustering
Week 8: Unsupervised learning: Hierarchical Clustering vs. Principal Component Analysis (PCA)
Week 9: Feature Engineering - Distributions/Scaling; PCA/PCR, regularization, feature reduction, feature expansion, mixed models, time series
Week 10: Neural Nets (Deep Learning) for regression vs. classification (as part of AI discussion)
Week 11: Capstone
- application topics
- hands-on exercises
- ?
