Please note! Course description is confirmed for two academic years, which means that in general, e.g. Learning outcomes, assessment methods and key content stays unchanged. However, via course syllabus, it is possible to specify or change the course execution in each realization of the course, such as how the contact sessions are organized, assessment methods weighted or materials used.

LEARNING OUTCOMES

Credits: 3

Schedule: 08.08.2022 - 26.08.2022

Teacher in charge (valid for whole curriculum period):

Teacher in charge (applies in this implementation): Marcela Acosta-Garcia, Alex Jung

Contact information for the course (applies in this implementation):

CEFR level (valid for whole curriculum period):

Language of instruction and studies (applies in this implementation):

Teaching language: English. Languages of study attainment: English

DETAILS

Substitutes for Courses
Prerequisites

FURTHER INFORMATION

Further Information
  • valid for whole curriculum period:

    This course will teach you some of the most widely used machine learning (ML) techniques. A focus will be on human-centered applications of ML methods that require high levels of privacy protection and transparency. The course includes lecture that teach basic principle of human-centered ML and its applications (such as elderly care). You will learn to implement privacy-preserving and transparent ML methods using few lines of Python during exercise sessions.