Week 00

Syllabus week! Tips for success.

Week 01

Machine learning tasks. Probability and statistics review. Computing tips, tricks, and resources.

Week 02

Introduction to supervised learning. Using linear regression for prediction. Estimating conditional means. Using lm(). Metrics for regression tasks. Data splits for model evaluation.

Week 03

k-nearest neighbors. Decision trees. Parametric versus nonparametric models. Using Categorical features and interactions.

Week 04

Bias-variance tradeoff. Regression overview.

Week 05

Introduction to classification. Probability models and the Bayes Classifier. k-nearest neighbors and decision trees again.

Week 06

Exam week!

Week 07

Specifics and metrics for binary classification. Logistic regression.

Week 08

Generative versus discriminative models. LDA, QDA, and Naive Bayes.

Week 09

Spring break!

Week 10

Resampling methods. Model tuning using cross-validation.

Week 11

Regularization with ridge and lasso. Dimension reduction.

Week 12

Ensemble methods. Bagging, random forest, and boosting.

Week 13

Exam week!

Week 14

Not much happened this week.

Week 15

Using machine learning for data analysis.

Week 16

Work on analyses!

Week 17

Work on analyses!