The goal of this course to familiarize the student with fundamental
concepts of machine learning in general and inductive rule learning in
particular. It will focus both on a predictive setting, where the goal
is to learn a set of rules that collectively make a prediction, and a
descriptive setting, where the goal is to learn a set of rules that
collectively explain the data. The learning techniques will be first
illustrated for concept learning tasks in propositional logic, but
later also extended to first-order logic as well as to structured
prediction tasks.
There are no prerequisites except for basic knowledge about
algorithms. The course is thus not only suited for computer science
students but for all students that have a strong interest in machine
learning in data analysis.

Rules - the clearest, most explored and best understood form of
knowledge representation - are particularly important for data mining,
because they offer the best tradeoff between human and machine
understandability. This course will present algorithms for automated
rule learning and discovery as investigated in classical machine
learning and modern data mining. We will start with algorithms for
learning single rules in propositional logic, move on to learning rule
sets with the covering or separate-and-conquer algorithm, inductive
logic programming algorithms for learning rules in first-order logic,
and discuss approaches that allow to make predictions in structured
output spaces. Elementary data mining algorithms such as association
rule discovery will also be covered, as well as essential concepts of
machine learning and data mining. It is thus suitable as a first
introduction into these research areas.
Most of the course will follow the book "Foundations of Rule Learning"
that will appear in Springer-Verlag in early 2012.
http://www.amazon.de/Rule-Learning-Essentials-Relational-Technologies/dp/3540751963

ECTS-Breakdown:
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20 hours lectures
53 hours preparation for exam.
2 hours examination
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75 hours: total
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The evaluation will depend on the examination at the end of term.