Alternative Learning Strategies

Many applications of machine learning do not fit exactly into the classical supervised learning setting. This course covers machine learning scenarios and strategies beyond the classical setting, which are relevant to contemporary machine learning research and applications. Examples are causal, meta, adversarial, multiple instance, neuromorphic and physics informed learning.

The final grade of the course consists of the following components:

• Written Exam (weighting 100%)

• Individual Assignment: assignments on theoretically and empirically evaluating and developing methods and critiquing a research paper (pass/fail)

Final grade calculation = Written Exam Grade (if pass for all three individual assignments)

A passing final grade for the course can only be earned when for all components at least a 5.0 is earned, and the weighted final grade is at least a 5.8.

In case of an insufficient final result, repair options may exist in accordance with Article 17A, Times and number of examinations, sub 1, of the Teaching and Examination Regulations, for:

• Witten Exam: Resit opportunity

• Individual Assignment: Repair opportunity

Disclaimer: information may change depending on unforeseen circumstances or measures (see: TER Art 2, sub 5).