- Start with reviewing Past Exam papers to see what I should expect.
- Last two lectures
- [ ]
- Intro
- Linear Classifier and Perceptrons
- Perceptron Learning Max Margin Classifier
- Soft Margin SVM
- ML Abstractions
- Decision Theory / Risk Minimization
- GDA, QDA, LDA, MLE
- Eigenvectors and Anisotropic / Visualizing Quadratic Form
- Anisotropic Gaussians: Maximum Likelihood Estimation, QDA, and LDA, Centering/ whitening/ decorrelating
- Regression
- Newton’s method, ROC Curve
- Statistical Justifications; the Bias-Variance Decomposition
- _Shrinkage: Ridge Regression, Subset Selection, and Lasso