Class homepage. Youtube videos.
  1. Machine Learning Setup
  2. k-Nearest Neighbors / Curse of Dimensionality
  3. Perceptron
  4. Estimating Probabilities from data
  5. Bayes Classifier and Naive Bayes
  6. Logistic Regression / Maximum Likelihood Estimation / Maximum a Posteriori
  7. Gradient Descent
  8. Linear Regression
  9. Support Vector Machine
  10. Empirical Risk Minimization
  11. Model Selection
  12. Bias-Variance Tradeoff
  13. Kernels
  14. Kernels continued
  15. Gaussian Processes
  16. k-Dimensional Trees
  17. Decision Trees
  18. Bagging
  19. Boosting
  20. Neural Networks
  21. Deep Learning / Stochastic Gradient Descent