Predictive Analytics in human-technology interaction (Winter semester 2024/25)

Lecturer: Prof. Dr. Dr. Marco Ragni

Assistant: Sara Todorovikj

Contents: The module provides a theoretical understanding and practical experience with basic concepts of data analysis, cognitive modeling, and machine learning for processing data in general and predicting human behavior in particular. The course will focus on general methods and basic algorithms, their advantages and disadvantages, and typical application areas.

Qualification goals:Students have successfully acquired methods and practical examples of statistical modeling and machine learning applied to large data sets. They are able to use this knowledge with the help of the acquired programming skills in the typical application areas of predictive behavior analysis and machine learning.

Requirements: Familiarity with linear algebra and probability theory methods is desirable. For the exercise basic knowledge of programming with Python is recommended, e.g. as taught in the module Introduction to programming with Python.

Target groups: Master Human Factors, Master Psychology, Master Sensors and Cognitive Psychology, Bachelor Applied Computer Science

OPAL: Link

Lecture

  Time: Monday, 09:15 - 10:45

  Place: 2/D301 (C24.301)

Exercise

  Time: Wednesday, 09:15 - 10:45

  Place: 2/W044 (C25.044)