Modulbeschreibung

AI Applications

Kürzel:
M_AIAp
Durchführungszeitraum:
FS/27
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:

  1. Understand the fundamentals of Deep Learning. Implement and train a neural network using a Deep Learning framework.
  2. Choose and apply appropriate Deep Learning techniques for a given task. Evaluate the results.
  3. Explain the fundamentals of Reinforcement Learning (RL, deep RL). Apply methods such as SARSA and deep Q-learning.
  4. Explain the basic building blocks of LLMs(e.g. transformers, attention) and learn how RAG architectures work.
  5. Analyze non-technical aspects of AI such as business impact and ethical implications.
Verantwortliche Person:
Dr. Lehmann Marco (LEMA)
Standort (angeboten):
Rapperswil-Jona, St.Gallen (Informatik Raster)
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 Retro STD_14_UG(Empfohlenes Semester: 4)
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 STD_23(Empfohlenes Semester: 4)
Wahl-Modul für Data Science STD_23 (VR)
Semester Empfehlung:
Keine Semester Empfehlung für dieses Modul vorhanden.
Bemerkungen:

The lecture will be streamed and recorded.

Kurse in diesem Modul