Introductory Quiz on Machine Learning Basics

Concept learning involves searching for a maximally specific hypothesis and adjusting it based on new observations to create a more general hypothesis.

The basic steps of decision tree learning involve building a tree structure based on data, searching for the best attribute to split the data, and recursively repeating the process on the subsets created by the split.

Bayesian learning involves using Bayes' theorem to update prior beliefs based on new data. Methods used in Bayesian learning include maximum likelihood estimation, the minimum description length principle, and the EM algorithm.