Notes on Learning from Data
Algorithms find the best ways to do things, but they don't explain "how" they came to those conclusions.
This is a common way to formulate ML problems, using target functions that we don't know but we want to approximate and learn.
The choice of algorithm (e.g. neural network) and the configuration of the algorithm (e.g. network topology and hyperparameters) define the space of possible hypothesis that the model may represent.
Learning is a search through the space of possible hypotheses for one that will perform well, even on new examples beyond the training set.
h (hypothesis): A single hypothesis, e.g. an instance or specific candidate model that maps inputs to outputs and can be evaluated and used to make predictions.
H (hypothesis set): A space of possible hypotheses for mapping inputs to outputs that can be searched, often constrained by the choice of the framing of the problem, the choice of model and the choice of model configuration.
