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Machine Learning 3.1 Introduction

This lecture summary discusses the foundations on which PAC Learning, a machine learning model, is based on. This model focuses on inductively learning an unknown target function, given only training examples of this target function and a space of candidate hypotheses. The questions that we will concern are how many training examples are sufficient to successfully learn the target function, and what level of confidence we may have regarding the chosen hypothesis and the probability of that it errs. We will show that it is possible to set quantitative bounds on these measures, depending on attributes of the learning problem.