9. Overview of Machine Learning Algorithms

Topics, Class Notes and Code		Assignments and Side Readings
Statistical algorithms LOESS regression and regularized regression Instance-based learning algorithms Naive Bayes based algorithm Tree-based Algorithms Tree-algorithm structure and building process Binary and multi-way splits Tree-based ensemble algorithms Notes and code Lecture Note [HTML] [PDF] Animated graph: [LOESS demo 1] [LOESS demo 2]		Optional Reading Assignments Tutorials on LOESS: [Explaination] [Example] Cleveland's Original Paper [PDF] Project Two: Supervised Learning Algorithms: Guidelines [HTML] [PDF] Part II - Regression and Classification Tree Algorithm Use the same data set you used last week to implement the decision tree algorithm. Create several candidate models and choose the best one via ROC analysis Use ROC analysis to choose the best one from logistic, Percetron, and decision tree algorithms. Due: 11:30 PM, Wednesday, 11/12/2025