Modulbeschreibung
Applied AI
Kurzzeichen:
Unterrichtssprache:
ECTS-Credits:
Arbeitsaufwand (h):
Leitidee:
The guiding idea is that AI is a powerful problem-solving tool and therefore every student needs to understand the fundamentals of AI to solve problems in their discipline. Hence, half of the module is used to teach the fundamentals of AI and the other half is used by the students to develop AI solutions to a problem from their discipline. The AI fundamentals are made understandable to students who neither have had higher mathematics, nor can program in a novel hands-on didactic approach.
Modulverantwortung:
Standort (angeboten):
Modultyp:
Bemerkungen:
Das Modul findet im Herbstsemester in Rapperswil-Jona (KW 37) statt.
Modulbewertung:
Leistungsnachweise und deren Gewichtung
Während der Unterrichtsphase:
Online or in-person presentation of an independently developed AI project in the student’s discipline.
Bewertungsart:
Inhalte
Angestrebte Lernergebnisse (Abschlusskompetenzen):
Sachkompetenzen
Die Studierenden können:
- explain why modern AI depends on Big Data and why AI is of fundamental importance
- explain why most interdisciplinary challenges can be addressed with AI and the resulting ethical implications
- explain the concept of probabilty intuitively and evaluate the performance of an AI algorithm
- distinguish between Supervised and Unsupervised Learning and between Regression and Classification
- identify AI applications from their own discipline and develop their own AI project
Selbstkompetenzen
Die Studierenden können:
- reflect on how AI will change their discipline
- reflect on how AI will change society at large
- reflect on how AI will change their personal lives
Sozialkompetenzen
Die Studierenden können:
- present their AI solutions and reflect on the different approaches and find the best solution together
- reflect on AI ethics and develop their own perspective which they then can defend in a group discussion
- present and argue for their own view on a pressing AI issue in a group discussion
Modul- und Lerninhalt:
Lernblock I
Introduction
- What is AI & why is AI important?
- Regression vs. Classification & Supervised vs. Unsupervised
Lernblock II
Develop intuition
- Probability is fundamendal
- Develop intuition with tables
- Find your own AI problem in your discipline with the help of the teaching staff
Lernblock III
Unsupervised Learning
- Clustering
- Principal Component Analysis
- Association Rules
Lernblock IV
Supervised Learning
- Linear Regression
- k-Nearest Neigbors Classification
- Support Vector Machines
- Decision Trees & Random Forests
Lehr- und Lernmethoden:
Inputreferate, Arbeiten in Teams an realen Problemstellungen, das Modul schafft Lernanlässe, aktive Mitarbeit steht im Vordergrund.