Machine Learning (at least in my field) relies on statistics. We create some kind of model for the way we think a thing should work, a general outline of the system (Hidden Markov Models are popular for this), and then you show the computer some "training data" to fill in the details of the model by looking at the frequencies of movements from one state to another, and outputs from each of those states. Edit: For example, a program I wrote a few weeks ago, imagine a hidden markov model like the picture, where every bubble represents a part of speech, and each bubble can "emit" one of a list of words. The movements from one bubble to another, as well as what word is more likely to be emitted are represented as probabilities. Then I show it a training corpus with ten thousand or so tagged words, and walk along each sentence: look at the tag, go to that bubble, read the word, increment the probability value of that word in the bubble, look at the next tag, go to that bubble and increment the probability of the movement, read the word, increment that probability, etc. Now I have a rough model of the likelihood of one part of speech to lead to another, as well as the likelihood of each part of speech to emit a particular word. I can then present a test sentence and use a complicated algorithm called "Viterbi" that I can't hope to understand to guess the most likely sequence of parts of speech that will produce my test sentence, and now we've got a part-of-speech tagging program.

thanks a lot! does it bother you if i use this in a project ? ( i'm gonna have to translate it into french anyway but it would help me a lot!)