Machine Learning @ Gray

Textbook: An Introduction to Machine Learning (ISLR) (free PDF download)

Week 1: ML Overview

Week 2: Regression 1: Advanced regression issues - distribution, scaling, outlier analysis, multicollinearity

• Practice Dataset
• Pick a house survey

Week 3: Regression 2:  feature reduction, expansion & engineering

• Download JASP

Week 4: Tree-based methods 1: tree, bagging, random forest

• Decision Tree Template

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

• Mixed Models
• Time Series: Forecasting Principles and Practice by Hyndman & Athanasopoulos

Week 10: Neural Nets (Deep Learning) for regression vs. classification (as part of AI discussion)

• Single Neuron Simulator

Week 11: Capstone

• application topics
• hands-on exercises
• ?