21C3 Schedule Release 1.1.7
21st Chaos Communication Congress
Lectures and workshops
|Start Time||16:00 h|
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Machine Learning in Science and Engineering
A Brief Introduction into Machine Learning with a few Application Examples
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.
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.
- 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.