Course catalog

19334717

WiSe 23/24: Seminar/Proseminar: Machine Learning for Process Control

Grégoire Montavon

Comments

Numerous real-world processes need to be kept under control in order to ensure safety or efficiency. Machine learning models are good candidates for this. They can for example detect shifts/anomalies/decalibrations/instabilities/etc. and possibly also predict which action needs to be taken on the process. The real-time nature of such tasks brings unique challenges from a ML perspective compared to classical application of ML. This seminar will explore relevant ML methods such as online/reinforcement learning and real-time data analysis. Use cases in manufacturing and intensive care will be covered. Students will select a few papers from a pool of thematically relevant research papers, which they will read and present over the course of the semester.

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16 Class schedule

Regular appointments

Fri, 16:00 - 18:00 Seminar/Proseminar: Machine Learning for Process Control 16 Class schedule

Fri, 2023-10-20 16:00 - 18:00

Seminar/Proseminar: Machine Learning for Process Control

Lecturers:
Prof. Dr.-Ing. Grégoire Montavon

Location:
T9/049 Seminarraum (Takustr. 9)

Fri, 2023-10-27 16:00 - 18:00

Seminar/Proseminar: Machine Learning for Process Control

Lecturers:
Prof. Dr.-Ing. Grégoire Montavon

Location:
T9/049 Seminarraum (Takustr. 9)

Fri, 2023-11-03 16:00 - 18:00

Seminar/Proseminar: Machine Learning for Process Control

Lecturers:
Prof. Dr.-Ing. Grégoire Montavon

Location:
T9/049 Seminarraum (Takustr. 9)

Fri, 2023-11-10 16:00 - 18:00

Seminar/Proseminar: Machine Learning for Process Control

Lecturers:
Prof. Dr.-Ing. Grégoire Montavon

Location:
T9/049 Seminarraum (Takustr. 9)

Fri, 2023-11-17 16:00 - 18:00

Seminar/Proseminar: Machine Learning for Process Control

Lecturers:
Prof. Dr.-Ing. Grégoire Montavon

Location:
T9/049 Seminarraum (Takustr. 9)

Fri, 2023-11-24 16:00 - 18:00

Seminar/Proseminar: Machine Learning for Process Control

Lecturers:
Prof. Dr.-Ing. Grégoire Montavon

Location:
T9/049 Seminarraum (Takustr. 9)

Fri, 2023-12-01 16:00 - 18:00

Seminar/Proseminar: Machine Learning for Process Control

Lecturers:
Prof. Dr.-Ing. Grégoire Montavon

Location:
T9/049 Seminarraum (Takustr. 9)

Fri, 2023-12-08 16:00 - 18:00

Seminar/Proseminar: Machine Learning for Process Control

Lecturers:
Prof. Dr.-Ing. Grégoire Montavon

Location:
T9/049 Seminarraum (Takustr. 9)

Fri, 2023-12-15 16:00 - 18:00

Seminar/Proseminar: Machine Learning for Process Control

Lecturers:
Prof. Dr.-Ing. Grégoire Montavon

Location:
T9/049 Seminarraum (Takustr. 9)

Fri, 2023-12-22 16:00 - 18:00

Seminar/Proseminar: Machine Learning for Process Control

Lecturers:
Prof. Dr.-Ing. Grégoire Montavon

Location:
T9/049 Seminarraum (Takustr. 9)

Fri, 2024-01-12 16:00 - 18:00

Seminar/Proseminar: Machine Learning for Process Control

Lecturers:
Prof. Dr.-Ing. Grégoire Montavon

Location:
T9/049 Seminarraum (Takustr. 9)

Fri, 2024-01-19 16:00 - 18:00

Seminar/Proseminar: Machine Learning for Process Control

Lecturers:
Prof. Dr.-Ing. Grégoire Montavon

Location:
T9/049 Seminarraum (Takustr. 9)

Fri, 2024-01-26 16:00 - 18:00

Seminar/Proseminar: Machine Learning for Process Control

Lecturers:
Prof. Dr.-Ing. Grégoire Montavon

Location:
T9/049 Seminarraum (Takustr. 9)

Fri, 2024-02-02 16:00 - 18:00

Seminar/Proseminar: Machine Learning for Process Control

Lecturers:
Prof. Dr.-Ing. Grégoire Montavon

Location:
T9/049 Seminarraum (Takustr. 9)

Fri, 2024-02-09 16:00 - 18:00

Seminar/Proseminar: Machine Learning for Process Control

Lecturers:
Prof. Dr.-Ing. Grégoire Montavon

Location:
T9/049 Seminarraum (Takustr. 9)

Fri, 2024-02-16 16:00 - 18:00

Seminar/Proseminar: Machine Learning for Process Control

Lecturers:
Prof. Dr.-Ing. Grégoire Montavon

Location:
T9/049 Seminarraum (Takustr. 9)

Back to list of modules

Location: T9/049 Seminarraum (Takustr. 9)

Schedule: Fr 16:00-18:00

Class starts on: 2023-10-20

Course language: English

Contact hours: 2

Number of participants: 30

Limited attendance: Yes

Attendance required: Yes

Modules where this course belongs

Information for pregnant and nursing students