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Instructors: Univ.-Prof. Dr. Michael Wand
Event type:
online: Lecture/practice class
Displayed in timetable as:
08.079.318
Hours per week:
4
Credits:
6,0
Language of instruction:
German
Min. | Max. participants:
- | -
Requirements / organisational issues:
Prerequisite: Modeling 1. The course does not require prior knowledge in machine learning (although this is certainly beneficial); the discussion of machine learning is self-contained. Basic math lectures (as well as some affinity to the topic) are very important (already for Modeling 1).
Contents:
Modeling 2 - "Statistical Data Modelling" is a research oriented specialty lecture. It provides a quick introduction to machine learning and then discusses in more detail ideas for modeling and understanding machine learning systems, in particular those based on deep neural networks (which appear to provide amazing performs but are still only partially understood).
See the German pages for more details.
Recommended reading list:
Richard O. Duda, Peter E. Hart, David G. Stork: Pattern Classification (Second Edition). Wiley & Sons 2000.
More suggestions during the course.
Digital teaching:
The course will be held fully digitally (video lectures, tutorials via video-conferencing). All further information is posted on the following web page:
https://luna.informatik.uni-mainz.de/mod2-21/
The website will go online shortly before the lecture starts. However, if you are interested, you can already examine the format of the previous course in the suffix /mod1-20.
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