Machine learning

A tentative table of contents:

• Bayesian Decision Theory
• minimum error rate classification
• discriminant functions and decision surfaces
• Parametric models and parameter estimation
• Non-parametric techniques
• K-Nearest neighbors classifier
• Decision trees
• Linear models
• Perceptron
• Logistic regression (Maxent)
• Large margin and kernel methods
• Generative versus discriminative modeling
• Sequence labeling and structure prediction
• Hidden Markov and MaxEnt Markov Models
• Sequence perceptron
• Conditional Random Fields
• Learning + Inference models