Machine Learning relates to building models from data.
Suppose we want to make a model for a complex process about which we have little knowledge (so hand-programming is not possible).
Solution: Get the computer to program itself by showing it examples of the behavior that we want.
Practically, we choose a library of models, and write a program that picks a model and tunes it to fit the data.
Criterion: a good model generalizes well to unseen data from the same process.
This method is known in various scientific communities under different names such as machine learning, statistical inference, system identification, data mining, source coding, data compression, etc.
Machine learning and the scientific inquiry loop.¶
Reinforcement Learning: Given sequences of inputs, actions from a
fixed set, and scalar rewards/punishments, learn to select action
sequences in a way that maximizes expected reward, e.g. chess and robotics. (This is more akin to learning how to design good experiments and is not covered in this course.)
Other stuff, like Preference Learning, learning to rank, etc. (also not covered in this course). Note that many machine learning problems can be (re-)formulated as special cases of either a supervised or unsupervised problem, which are both covered in this class.
Output from coder is much smaller in size than original, but if coded signal if further processed by a decoder, then the result is very close (or exactly equal) to the original.