1. The basic ideas of Machine Learning
1.1. Inference and prediction
1.2. Supervised vs unsupervised learning
1.3. Bias-variance trade-off
1.4. Evaluation
2. Linear Model Selection and Regularization
2.1. Review of the linear model
2.2. Subset selection
2.3. Ridge regression
2.4. Lasso
3. Imputation
4. Poisson regression
5. Logistic Regression
6. Cross Validation
7. Trees
8. KNN
9. K-Means
10. Clustering
11. Linear Discriminant Analysis
12. Principal Components Analysis
Apuntes preparados por Pablo Arés