Computational Learning Theory and Beyond
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. The overall question of the course will be how to choose an appropriate model and what consequences the choice of the model has.
October 6, 2020 - October 27, 2020
Language: English

Course information

In this T-shaped course you will be introduced to computational learning theory and get a glimpse of other research towards a theory of artificial intelligence. "T-shaped" means that on the one hand we will concentrate on different learning models in depth, on the other hand we want to give a broad overview and invite experts from other AI projects to show what else can be done in AI.

The focus is on learning from informant, a formal model for binary classification, for example by a support vector machine. Illustrating examples are linear separators and other uniformly decidable sets of formal languages. Due to results by Gold the learning process can be assumed consistent. Another legitimate assumption is performing mind-changes only when observing an inconsistency.

After the proofs of the latter observations, the model is adjusted towards the setting of deep learning. This incremental model has less learning power than the full-information variant by a fundamental proof technique due to Blum and Blum. You will apply this technique to separate consistency. Finally, we outline why this model suggests to design incremental learning algorithms that update their currently hypothesized classifier, even though it is consistent with the observed datum.

Beyond these models, you will get digestible insights into other approaches towards a theory of AI. These include stable matchings, evolutionary algorithms, fair clustering, game theory, low-dimensional embeddings, submodular optimization and 3-satisfiability.
Further, more models in computational learning theory are being discussed.

Course characteristics:

  • Language: English
  • Starting from: October 6, 2020
  • Course end: October 27, 2020
  • Duration: 2 weeks + 1 examination week
  • Course requirements: familiarity with mathematical notation (basic studies at the university)

Target audience

  • everyone who is interested in models of AI and like mathematical accuracy/evidence
  • students who follow mathematics lectures (e.g. mathematics, physics, computer science, cognitive science/systems, computational linguistics)
  • (hobby) scientists who have already come into contact with formal mathematics (definitions and proofs)

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The course is free. Just register for an account on openHPI and take the course!
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Learners enrolled: 2045

Certificate Requirements

  • Gain a record of achievement by earning at least 50% of the maximum number of points from all graded assignments.
  • Gain a confirmation of participation by completing at least 50% of the course material.

Find out more in the certificate guidelines.

This course is offered by

Karen Seidel

Karen Seidel is a PhD student in the Research Group for Algorithm Engineering at the Hasso-Plattner-Institute (HPI) in Potsdam, Germany. Her previous research in Artificial Intelligence focuses on modelling learning with Automata and Turing Machines. She graduated from the University of Bonn with a master in mathematics and worked in Mathematical Logic and Cognitive Mathematics at the Universities of Münster, Osnabrück and Cologne. She has wide-ranging experience in teaching and works at the HPI since 2017.

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