Computational Learning Theory and Beyond

Target Groups:

everyone who is interested in models of AI and like mathematical accuracy/evidence.

Professional Requirements:

Language: English. Course requirements: familiarity with mathematical notation (basic studies at the university).

Technical Requirements:

Keine besonderen Anforderungen.

Classification of the Federal Employment Agency:

keine Angaben

Contents

In this course you will be introduced to computational learning theory and get a glimpse of other research towards a theory of artificial intelligence.
Our starting point will be a hands-on binary classification task. Basically, this is the challenge of classifying the elements of a given set into two groups (predicting which group each one belongs to) on the basis of given labeled data. Thus the goal of the supervised machine learning algorithms is to derive a correct classification rule. Our interest lies in strategies that work not only for one specific classification task but more universally for a pre-specified set of such. You will get to know a formalization of the aforementioned notions and see illustrating examples. In the main part, you will get to know different learning models which are all based on a modular design. By investigating the learning power of these models and the learnability of the prominent set of half-spaces, we also give arguments for how to choose an appropriate one.