Modulbeschreibung

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 

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