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Instructors: Dr. Kristof Schmieden
Event type:
Lecture
Displayed in timetable as:
08.128.614
Hours per week:
2
Credits:
3,0
Language of instruction:
German
Min. | Max. participants:
- | -
Requirements / organisational issues:
The lecture times will be decided in the first week of lectures, in agreement with all participants.
The suggested times are: Tuesday or Wednesday afternoon, 2 consectuive hours between 1pm and 6pm.
Language: Upon request the lecture can be held in english.
Contents:
The Math-Basics of mashine learning will only be discussed briefly.
The focus of the lecture is put on the practical implementation and usage of ML tools for classification and regression. To this end we will implement many examples using Python & Keras and explore their limits. Example data used will be mainly from particle physics, however no detailed knowledge of the physics processes is needed to follow the lecture.
During the course of the lecture you will learn amongst other:
- When a ML classificator is useful
- How a ML tool is implemented and used
- How to see if a training is robust or not
- How to estimate uncertainties on ML methods
- How to optimise ML classifications / regressions
Additional information:
Basic knowlegde in python is useful
Digital teaching:
LMS: https://lms.uni-mainz.de/moodle/course/view.php?id=85305
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