After successful completion of the course, students are able to...
- Identify threats to privacy of individuals in machine learning datasets
- Select fitting solutions for privacy-preserving machine learning
- Understand attack vectors on machine learning models, and how attacls can be detected and mitigated
- Select fitting concepts for explainable and interpretable machine learning
The course contains classroom lectures and exercises. Exercises include the application of privacy-preserving, secure and explainabel machine learning techniques for various data sets and implementation of thses techniques. The exercises are prepared at home and will be presented/discussed during the exercise classes.
Dates:
5.3. 2020: Preliminary talk (Vorbesprechung) & intro
For all other dates, please see TUWEL! Note that the lecture won't take place every week!
- Solving of exercises regarding experiments in secruity, privacy and explainability of machine learning, using a software toolkit of the student's choice (e.g. Python scikit-learn, Matlab, R, WEKA, ...)
- Written exam at the end of the semester
184.702 Machine Learning