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Spezialvorlesung "Maschinelles Lernen and Data Mining"
Prof. Dr. Luc De Raedt
Co-organizers: Dr. Stefan Kramer , Dr. Christoph Helma, Dipl.-Inf. Kristian
Kersting
Tuesday 16-18 o'clock, Thursday 11-12 o'clock,
Room: SR 101-00-010/014
Exercises:Thursday 12-13, Room:
SR 101-00-010/014
Credit points (Kreditpunkte): 6
The ability to learn is central to intelligence. Therefore machine learning
is one of the key areas in artificial intelligence. Machine learning is
concerned with building agents that can learn in one way or another. Various
techniques for learning will be surveyed in this course. This includes
:
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symbolic learning
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neural networks
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genetic algorithms
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decision trees
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bayesian networks
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versionspaces
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inductive logic programming
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computational learning theory
The most popular applications of machine learning are in the analysis of
data. In recent years, this has been the key topic of data mining. This
is a new science, which aims at discovering (nuggets of) knowledge from
data. To realize this, it combines machine learning, statistics and database
techniques. Special emphasis is laid on analysing large datasets. Techniques
from data mining and their applications will also be surveyed in this course.
This includes :
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the knowledge discovery process
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association rule discovery
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handling large data sets
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...
This course is especially relevant to BioInformatics as the analysis
and interpretation of
(large amounts of) data is one of the key problems in BioInformatics.
Furthermore, techniques from the field of machine learning and data mining
complement the more traditional data analysis techniques from statistics.
Various applications in BioInformatics will be discussed in the course.
Throughout the course the book by Tom Mitchell, Machine Learning,
McGraw Hill, 1997 will be used and complemented by other materials.
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