Studienhandbuch | UE Hands-on AI I

Inhalt

^(*) Leider ist diese Information in Deutsch nicht verfügbar.

Workload	Ausbildungslevel	Studienfachbereich	VerantwortlicheR	Semesterstunden	Anbietende Uni
1,5 ECTS	B1 - Bachelor 1. Jahr	Artificial Intelligence	Rainer Dangl	1 SSt	Johannes Kepler Universität Linz

Detailinformationen

Quellcurriculum

Bachelorstudium Artificial Intelligence 2025W

Lernergebnisse

Kompetenzen
^(*)Students are able to handle, preprocess, and visualize different types of datasets, and apply foundational deep learning techniques for practical problem-solving. Students are capable of developing, training, and optimizing artificial neural networks using deep learning frameworks like PyTorch, including understanding and applying techniques like gradient descent, batch normalization, and dropout.

Fertigkeiten	Kenntnisse
^(*) Handling and understanding of different kinds of data sets Visualization, dimensionality reduction and clustering of data Moving from Linear Regression to Logistic Regression and Artificial Neural Networks Understanding gradient descent Using Deep Learning frameworks like PyTorch Playing around with the first Artificial Neural Networks Introducing Convolutional Neural Networks Tips and tricks for making networks learn (batch normalization, dropout, transfer learning, etc.)	^(*)Students possess foundational knowledge of deep learning concepts, including neural networks, gradient descent, data visualization, and handling of various types of datasets. They understand the use of AI frameworks like PyTorch and are familiar with optimization techniques and deep learning architectures like Convolutional Neural Networks.

Beurteilungskriterien

^(*)Online Assignments

Abhaltungssprache

Englisch

Lehrinhalte wechselnd?

Nein

Äquivalenzen

^(*)in collaboration with 536AIBAHO1V20: VL Hands-on AI I (1.5 ECTS) equivalent to
536AIBAHO1K19: KV Hands-on AI I (3 ECTS)

Präsenzlehrveranstaltung
Teilungsziffer	35
Zuteilungsverfahren	Direktzuteilung