| Introduction
| Probability Review
| Bayesian Decision Theory 1
| Bayesian Decision Theory 2
| Parametric Methods for Density
| Nonparametric Methods for Density Estimation
| Linear Discriminant Functions; Perceptron
| Kernel Methods
| Support Vector Machines
| Ensemble Methods
| Image Classification Pipeline
| Loss Functions and Optimization
| Back Propagation and Neural Networks
| Convolutional Neural Networks