GGS-Workshop: Machine Learning with Python (Advanced course for participants of last year's "Introduction to Machine Learning")
- https://www.uni-giessen.de/de/fbz/zentren/ggs/veranstaltungen/index_html/wise-2021_22/machinelearningII
- GGS-Workshop: Machine Learning with Python (Advanced course for participants of last year's "Introduction to Machine Learning")
- 2021-10-01T09:00:00+02:00
- 2021-10-02T17:00:00+02:00
01.10.2021 09:00 bis 02.10.2021 17:00 (Europe/Berlin / UTC200)
Präsenz - Raum 002, Licher Str. 68
Doctoral candidates and postdocs who have undergone the GGS course "Introduction to Machine Learning" in September 2020 or March 2021
| Instructor: | Dr Tobias Keller | |
| Dates: |
October 1, and 2, 2021 (Friday and Saturday!) (9.00 am - 5.00 pm) |
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Max. participants: |
10 | |
| Course language: | English | |
| Registration Deadline: |
September 15, 2021 |
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| ECTS: | 3 |
This course is a follow up of the former GGS course "Introduction to Machine Learning".
For registration you either must have undergone this course or be able to prove similar knowlege.
Bitte beachten Sie: Um für alle Beteiligten ein größtmögliches Maß an Sicherheit zu gewährleisten, können entsprechend der 3G-Regelung nur Personen am Kurs teilnehmen, die geimpft, genesen oder getestet (tagesaktuell) sind.
Personen, die nicht geimpft oder genesen sind, müssen jeweils tagesaktuelle Testzertifikate aus einem Testzentrum (digital oder in Papierform) vorweisen. Vor Ort durchgeführte Selbsttests werden nicht anerkannt werden.
Die JLU stellt weder Corona-Tests für nicht-genesene und nicht-geimpfte Personen noch die entsprechende Test-Infrastruktur zur Verfügung. Siehe dazu auch die JLU-Regeln unter: https://www.uni-giessen.de/coronavirus/faq/faq#dreig
Objectives
In this follow-up course, participants reinforce and add to their knowledge of machine learning concepts solving classification and regression problems with Python. Through application, they strengthen their understanding of machine learning algorithms, evaluation techniques and measures, and learn how to combine data preparation and algorithms in machine learning pipelines.
In addition, participants will be introduced to more advanced concepts, focusing on model selection (including hyperparameter tuning and algorithm selection) and approaches to understanding “black box” models (also known as explainable AI, “XAI”).
The course will follow on seamlessly from the former course "Introduction to Machine Learning".
You can find more details in the syllabus "Machine Learning with Python"