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Control, Machine Learning and Numerics (CML)10 ECTS (englische Bezeichnung: Control, Machine Learning and Numerics)
(Prüfungsordnungsmodul: Control, machine learning and numerics)
Modulverantwortliche/r: Enrique Zuazua Lehrende:
Enrique Zuazua
Startsemester: |
SS 2022 | Dauer: |
1 Semester | Turnus: |
jährlich (SS) |
Präsenzzeit: |
75 Std. | Eigenstudium: |
225 Std. | Sprache: |
Englisch |
Lehrveranstaltungen:
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CML: Control, Machine Learning and Numerics
(Vorlesung, 2 SWS, Enrique Zuazua et al., Mo, 8:00 - 10:00, HA)
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Practical sessions: CML: Control, Machine Learning and Numerics
(Übung, Yongcun Song, Blockveranstaltung 28.4.2022-9.6.2022 Do, Blockveranstaltung 7.7.2022-28.7.2022 Do, 14:00 - 17:00, HA, (außer Do 21.7.2022))
Inhalt:
This course covers:
Equations (ODE) and Partial Differential Equations (PDE),
including controllability, observability, and some of the most
fundamental work that has been done in the subject in recent
years.
of control techniques for the analysis of Deep Neural Networks as
a tool to address, for instance, the problem of Supervised
Learning.
ODE and PDE, and machine learning.
Lernziele und Kompetenzen:
Students are able to:
understand some basic theory on control and machine learning.
learn about recent advances on control and machine learning.
implement some computational techniques using their own or
specified software and critically evaluate the results,
convincing manner, making use of appropriate presentation
techniques.
Verwendbarkeit des Moduls / Einpassung in den Musterstudienplan:
- Computational and Applied Mathematics (Master of Science)
(Po-Vers. 2019w | NatFak | Computational and Applied Mathematics (Master of Science) | Gesamtkonto | Specialisation: Modeling and applied analysis (MApA) and optimization (Opti) | Control, machine learning and numerics)
- Computational and Applied Mathematics (Master of Science)
(Po-Vers. 2019w | NatFak | Computational and Applied Mathematics (Master of Science) | Gesamtkonto | Specialisation: Numerical analysis and simulation (NASi) and optimization (Opti) | Control, machine learning and numerics)
Dieses Modul ist daneben auch in den Studienfächern "Data Science (Master of Science)", "Wirtschaftsmathematik (Master of Science)" verwendbar. Details
Studien-/Prüfungsleistungen:
Control, machine learning and numerics (Prüfungsnummer: 50931)
- Prüfungsleistung, variabel, benotet, 10 ECTS
- Anteil an der Berechnung der Modulnote: 100.0 %
- weitere Erläuterungen:
Project work with presentation and report
- Prüfungssprache: Englisch
- Erstablegung: SS 2022
1. Prüfer: | Enrique Zuazua |
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UnivIS ist ein Produkt der Config eG, Buckenhof |
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