Modulbeschreibung

AI Applications

Kürzel:
M_AIAp
Durchführungszeitraum:
FS/22
ECTS-Credits:
4
Lernziele:

In this module, we focus on advanced AI techniques and their application in software projects. We will discuss and implement different deep learning architectures. 

After successful completion of this module the students are able to:
• implement and train different deep learning architectures in Tensorflow/Keras
• explain what a computational-graph is and how it is used by neural networks
• choose and apply appropriate deep learning techniques for solving different tasks (e.g image classification or time-series analysis)
• approach an AI project from analysis to deployment and monitoring
• use a pretrained network in a software project

Verantwortliche Person:
Prof. Dr. Lehmann Marco
Standort (angeboten):
Rapperswil-Jona
Empfohlene Module:
Zusätzlich vorausgesetzte Kenntnisse:

This module is taught in English. Most of the AI Literature is in English. An intermediate level is recommended. The students are free to write reports in German or English. The exam questions will be in English, the students are free to write answers in German or English.

 

Skriptablage:
Modultyp:
Wahlpflicht-Modul für Informatik STD_14(Empfohlenes Semester: 4)
Wahl-Modul für Data Science STD_14 (PF)
Wahlpflicht-Modul für Informatik STD_21(Empfohlenes Semester: 4)
Wahl-Modul für Data Science STD_21 (PF)
Wahlpflicht-Modul für Informatik Retro STD_14_UG(Empfohlenes Semester: 4)

Kurse in diesem Modul