Classical Supervised Learning

1 Linear Regression
1.1 Fitting a Line
2 Generalization
2.1 Overfitting and Underfitting
2.2 The Bias-Variance Tradeoff
2.3 Regularization
3 Logistic Regression
3.1 Logistic Regression
4 Generative Classifiers
4.1 Naive Bayes
5 Geometric Methods
5.1 k-Nearest Neighbors
5.2 Support Vector Machines
6 Trees and Ensembles
6.1 Decision Trees
6.2 Random Forests
6.3 Boosting
7 Evaluating a Classifier
7.1 Evaluating a Classifier