Fuzzy coding, decisions, learning
This is more for overview of my own than for teaching or exercise.
Other data analysis, data summarization, learning

Contents
Fuzzy coding, decisions, learning
This article/section is a stub — probably a pile of halfsorted notes, is not wellchecked so may have incorrect bits. (Feel free to ignore, fix, or tell me) 
On bias
Methods / algorithms / searchers
Decision trees
ID3
Pruning (ID3, others)
Rule Postpruning; C4.5
Instancebased learning
Bayesian learning
This article/section is a stub — probably a pile of halfsorted notes, is not wellchecked so may have incorrect bits. (Feel free to ignore, fix, or tell me) 
Bayesian learning is a general probablistic approach, mostly specifically used as a probablistic classifier.
Mathematically it is based on any observable attribute you can think of, and the math requires Bayesian inversion (see below).
Many basic implementations also use the Naive Bayes assumption (see below), because it saves a lot of computation time, and seems to work almost as well in most cases.
Bayesian classifier
Bayes Optimal Classifier
Naive Bayes Classifier
Bayesian (Belief) Network
Some classifiers
This article/section is a stub — probably a pile of halfsorted notes, is not wellchecked so may have incorrect bits. (Feel free to ignore, fix, or tell me) 
 Parzen classifier
 Backpropagation classifier
Evaluating classifiers
Support Vector Machines
Markov Models, Hidden Markov Models
This article/section is a stub — probably a pile of halfsorted notes, is not wellchecked so may have incorrect bits. (Feel free to ignore, fix, or tell me) 
Something like (the simplest possible) Bayesian Belief Networks, but geared to streams of data. Can be seen as a state machine noting the likeliness of each next step based on a number of preceding steps.
The hidden variant only shows its output (and hides the model that produces it), the nonhidden one shows all of its state.
Simple ones are firstorder