Wartungsarbeiten: File-Server So/Mo 26. Mai 22:00 - 27. Mai 07:00 | Aufgrund von Wartungsarbeiten an den File-Servern können in diesem Zeitraum keine Dateien hochgeladen werden. Die Dateien auf ILIAS können aber weiterhin heruntergeladen/angesehen werden.
Symbol Kurs

470273-HS2023: CAS Advanced Machine Learning

Certificate of Advanced Studies in Advanced Machine Learning (CAS AML) - 2023/2024

Reiter

Allgemeine Informationen

Kursbeschreibung
In many disciplines, the amount of available data and the computing capacity are growing rapidly. This enables the application of machine learning methods on tasks previously being reserved for humans. The format is designed to align with the participants’ main study and or professional activities. The teaching and learning approaches are team and discussion oriented and designed to develop practical competency. It is structured in six modules which are offered in blocks and Fridays' afternoon. The CAS is at a university master level and programming and some machine learning skills from education or profession are required, e.g. the CAS Applied Data Science.

Lernziele und Links

Lernziele
Course competence is developed throughout six modules and a CAS project work. On completion the graduates will (be able to):
- design, tune, train and measure performance of neural networks with advanced deep -learning libraries
- understand the inner mechanisms of neural networks during training
- familiar with active research in machine learning
- understand and communicate scientific publications on machine learning and artificial intelligence
- familiar with the philosophy and ethics of extended and artificial intelligence
- familiar with one or more applied machine learning domains, the main mathematical methods for data science and machine learning

Beschreibung

Certificate of Advanced Studies in Advanced Machine Learning (CAS AML) - 2023/2024

Allgemein

Sprache
Englisch
Copyright
This work has all rights reserved by the owner.

Tutorielle Betreuung

[c.beisbart]

PD Dr Sigve Haug

Adresse

Sidlerstrasse 5
3012 Bern
Switzerland

Institution / Abteilung

University of Bern / Data Science Lab and Mathematical Institute

Kontakt

E-Mail: sigve.haug@unibe.ch

[m.vladymyrov1]

[l.brigato]

Verfügbarkeit

Zugriff
1. Mär 2023, 16:50 - 1. Apr 2025, 13:55
Aufnahmeverfahren
Wenn Sie das Kurspasswort von einem Kursadministrator erhalten haben, können Sie in diesen Kurs beitreten.
Zeitraum für Beitritte
Unbegrenzt
Minimale Teilnehmeranzahl
10
Freie Plätze
16

Für Kursadministratoren freigegebene Daten

Daten des Persönlichen Profils
Benutzername
Vorname
Nachname
E-Mail

Zusätzliche Informationen

Link zu dieser Seite