• Course Overview: This course introduces a set of methods in Machine learning. We do not discuss computation issues.

• Table of Contents

  1. Training and Machine Learning
     • Undersmoothing
     • Error decomposition
     • Complexity

  2. Classification
     • Support vector machine
     • Bayes rule

  3. Neural Networks
     • Shallow NN
     • Deep learning

  4. Boosting
     • AdaBoost
     • Ensemble weak learners

  5. Trees
     • Regression trees
     • Classification trees
     • Boosting trees

  6. Random Forests
     • Bootstrap
     • Random forests 

  7. Undirected Graphs
     • Markov networks
     • Gaussian likelihood
     • Graphical Lasso