Apa itu Training, validation and test sets; hyperparameter?

The training set is the data on which $\theta$ is fitted. A hyperparameter is a choice the fit does not make itself (a penalty, a tree depth, a number of layers, a learning rate); the validation set is data held out from the fit on which hyperparameters are chosen. The test set is data used once, after every choice is made, to estimate how the chosen model will perform.