Machine Learning in Science and Engineering
Item
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Title
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Machine Learning in Science and Engineering
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A Brief Introduction into Machine Learning with a few Application Examples
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Description
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A broad overview about the current stage of research in Machine Learning starting with the general motivation and the setup of learning problems and discussion of state-of-the-art learning algorithms for novelty detection, classification and regression. Additionally, machine learning methods used for spam detection, intrusion detection, brain computer interace and biological sequence analysis are outlined.about this event: http://www.ccc.de/congress/2004/fahrplan/event/44.en.html
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content
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The talk is going to have three parts:
(a) What is Machine Learning about?
This includes a general motivation, the setup of learning problems (suppervised vs unsupervised; batch vs online). I'll mention typical examples (e.g. OCR, Text-classification, medical Diagnosis, biological sequence analysis, time series prediction) and use them as motivation.
(b) What are state-of-the-art learning techniques?
With a minimal amount of theory, I'll describe some methods including a currently very successful and easily applicable method called Support Vector Machines. I'll provide references to standard literature and implementations of these algorithms.
(c) I'll discuss a few applications in greater detail, to show how Machine Learning can be successfully applied in practice.
These include:
spam detection
face detection and reconstruction
intelligent hard disk spin (online learning)
biological sequence analysis & drugs discovery
network intrusion detection
brain computer interface
analysis of questionnaires (Fraud detection, fake interviewer identification)
I try not present the material as self-contained as possible, but I will require some math knowledge on part (b). I mainly want to bring ideas across and will provide references to papers and web-resources for further reading about the details of the methods and applications.
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Date Issued
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27 December 2004, 16:00:00 +01:00
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Extent
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0:55:39
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Type
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video/mp4
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Tag
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21c3
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Science
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Identifier
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ark:/45490/bTkESg
21C3 Website
Chaos Computer Club e. V.
