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Machine Learning (Lecture) (CS-BP-NI, CS-BWP-AI, CS-BWP-INF, CS-BWP-NI)
Prof. Dr.-Ing. Gunther Heidemann
Ulf Krumnack, Ph.D.
Lukas Niehaus
Veranstaltungstyp: Vorlesung und Übung
TeilnehmerInnen: ab 4. Semester
Beschreibung:
Prerequisites: None
Being a mainly academic topic about 20 years ago, Machine Learning has become a discipline of major impact on both science and engineering by today. This course introduces the basics of Machine Learning and Data Mining. Major topics are concept learning, decision trees, problems of data in high dimensional representations, clustering algorithms, linear and nonlinear dimension reduction, artificial neural networks (e.g. multilayer perceptrons, RBF networks, self-organizing maps), classification methods, reinforcement learning, modeling uncertainty and temporal probability models.
Erstes Treffen:
Dienstag, 02.04.2024 14:00 - 16:00, Ort: 93/E31
Ort: 93/E31: Di. 14:00 - 16:00 (14x) Mi. 10:00 - 12:00 (13x) Do. 10:00 - 12:00 (13x), 32/110: Donnerstag, 04.07.2024 10:00 - 12:00
Semester: SoSe 2024
Zeiten:Di. 14:00 - 16:00 (wöchentlich) - Übung, Ort: 93/E31, Mi. 10:00 - 12:00 (wöchentlich) - Vorlesung, Ort: 93/E31, Do. 10:00 - 12:00 (wöchentlich) - Vorlesung, Ort: 93/E31, Termine am Donnerstag, 04.07.2024 10:00 - 12:00, Ort: 32/110
Leistungsnachweis:
Veranstaltungsnummer:
8.3072
ECTS-Kreditpunkte:
12
Bereichseinordnung:
Veranstaltungen > Cognitive Science > Bachelor-Programm Veranstaltungen > Cognitive Science > Master-Programm Schnupperstudium > Schnupper Uni > Cognitive Science Courses in English > Human Sciences (e.g. Cognitive Science, Psychology) Uni für Alle > Cognitive Science
Prof. Dr.-Ing. Gunther Heidemann
Ulf Krumnack, Ph.D.
Lukas Niehaus
Veranstaltungstyp: Vorlesung und Übung
TeilnehmerInnen: ab 4. Semester
Beschreibung:
Prerequisites: None
Being a mainly academic topic about 20 years ago, Machine Learning has become a discipline of major impact on both science and engineering by today. This course introduces the basics of Machine Learning and Data Mining. Major topics are concept learning, decision trees, problems of data in high dimensional representations, clustering algorithms, linear and nonlinear dimension reduction, artificial neural networks (e.g. multilayer perceptrons, RBF networks, self-organizing maps), classification methods, reinforcement learning, modeling uncertainty and temporal probability models.
Erstes Treffen:
Dienstag, 02.04.2024 14:00 - 16:00, Ort: 93/E31
Ort: 93/E31: Di. 14:00 - 16:00 (14x) Mi. 10:00 - 12:00 (13x) Do. 10:00 - 12:00 (13x), 32/110: Donnerstag, 04.07.2024 10:00 - 12:00
Semester: SoSe 2024
Zeiten:Di. 14:00 - 16:00 (wöchentlich) - Übung, Ort: 93/E31, Mi. 10:00 - 12:00 (wöchentlich) - Vorlesung, Ort: 93/E31, Do. 10:00 - 12:00 (wöchentlich) - Vorlesung, Ort: 93/E31, Termine am Donnerstag, 04.07.2024 10:00 - 12:00, Ort: 32/110
Leistungsnachweis:
Veranstaltungsnummer:
8.3072
ECTS-Kreditpunkte:
12
Bereichseinordnung:
Veranstaltungen > Cognitive Science > Bachelor-Programm Veranstaltungen > Cognitive Science > Master-Programm Schnupperstudium > Schnupper Uni > Cognitive Science Courses in English > Human Sciences (e.g. Cognitive Science, Psychology) Uni für Alle > Cognitive Science