Control, Machine Learning and Numerics (CML)10 ECTS
(englische Bezeichnung: Control, Machine Learning and Numerics)
(Prüfungsordnungsmodul: Control, machine learning and numerics)

Lectures:

• • CML: Control, Machine Learning and Numerics
    (Vorlesung, 2 SWS, Enrique Zuazua et al., Mon, 8:00 - 10:00, HA)
  • Practical sessions: CML: Control, Machine Learning and Numerics
    (Übung, Yongcun Song, block seminar 28.4.2022-9.6.2022 Thu, block seminar 7.7.2022-28.7.2022 Thu, 14:00 - 17:00, HA, (außer Thu 21.7.2022))

Inhalt:

This course covers:

• several topics related to the control of Ordinary Differential

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.

• an introduction to Machine Learning, focusing mainly on the use

of control techniques for the analysis of Deep Neural Networks as a tool to address, for instance, the problem of Supervised Learning.

• some classical computational techniques related to the control of

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,

• set out their approaches and results in a comprehensible and

convincing manner, making use of appropriate presentation techniques.

Verwendbarkeit des Moduls / Einpassung in den Musterstudienplan:

1. Wirtschaftsmathematik (Master of Science)
   (Po-Vers. 2019w | NatFak | Wirtschaftsmathematik (Master of Science) | Gesamtkonto | Mathematische Wahlpflichtmodule | Specialisation: Modeling and applied analysis (MApA) and optimization (Opti) | Control, machine learning and numerics)
2. Wirtschaftsmathematik (Master of Science)
   (Po-Vers. 2019w | NatFak | Wirtschaftsmathematik (Master of Science) | Gesamtkonto | Mathematische Wahlpflichtmodule | Specialisation: Numerical analysis and simulation (NASi) and optimization (Opti) | Control, machine learning and numerics)

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