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.
Rhythmus | Tag | Uhrzeit | Format / Ort | Zeitraum | |
---|---|---|---|---|---|
wöchentlich | Di | 12-14 | CITEC | 10.10.2022-03.02.2023
nicht am: 01.11.22 / 27.12.22 / 03.01.23 |
|
einmalig | Di | 10-12 | - | 14.02.2023 | Erstklausur |
einmalig | Di | 10-12 | - | 21.03.2023 | Zweitklausur |
Verstecke vergangene Termine <<
Modul | Veranstaltung | Leistungen | |
---|---|---|---|
39-Inf-ML_ver1 Grundlagen Maschinelles Lernen | Grundlagen Maschinellen Lernens | unbenotete Prüfungsleistung
benotete Prüfungsleistung |
Studieninformation |
Die verbindlichen Modulbeschreibungen enthalten weitere Informationen, auch zu den "Leistungen" und ihren Anforderungen. Sind mehrere "Leistungsformen" möglich, entscheiden die jeweiligen Lehrenden darüber.
Studiengang/-angebot | Gültigkeit | Variante | Untergliederung | Status | Sem. | LP | |
---|---|---|---|---|---|---|---|
Studieren ab 50 | - | - | - | - | - | - |
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