Machine Learning in Science and Engineering
Item
- Title
- Machine Learning in Science and Engineering
- A Brief Introduction into Machine Learning with a few Application Examples
- Description
- 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
- content
-
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. - Date Issued
- Beteiligte Person
- Gunnar Rätsch
- Is Referenced By
-
21C3 Website
- Creator
-
Chaos Computer Club e. V.
- Rights Holder
-
Chaos Computer Club e. V.
- Extent
- 0:55:39
- Type
- video/mp4
- Tag
- 21c3
- Science
- Identifier
- ark:/45490/bTkESg
