The lecture will introduce basic techniques in machine learning, in particular probability based methods. It starts by discussing methods for classification and subsequently (re)-introducing regression in a Bayesian framework as maximum likelihood and maximum a posteriori estimation and proceeds by regarding parameter estimation as a probabilistic process. It introduces concept learning and some of its most popular and widespread applications, e.g. classifaction of data given in form of list of attributes and decision trees. Further topics are unsupervised problems such as clustering and ethical questions in machine learning.
Good knowledge of mathematics as taught in the first semesters is indispensible.
We also recommend knowledge about basics of probability theory.
The lecture is part of the international track and will be given in English.
There will be lecture notes available.
Frequency | Weekday | Time | Format / Place | Period |
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Module | Course | Requirements | |
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39-Inf-ML_ver1 Grundlagen Maschinelles Lernen | Grundlagen Maschinellen Lernens | Ungraded examination
Graded examination |
Student information |
The binding module descriptions contain further information, including specifications on the "types of assignments" students need to complete. In cases where a module description mentions more than one kind of assignment, the respective member of the teaching staff will decide which task(s) they assign the students.
Degree programme/academic programme | Validity | Variant | Subdivision | Status | Semester | LP | |
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Studieren ab 50 |
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