Winter 2023:
As machine learning increasingly pervades more and more aspects of our life, hitherto often ignored questions regarding social responsibility and trustworthiness of machine learning and AI emerge as mainstream topics that pose deep and challenging mathematical problems. How do we take fairness and transparency into account while developing machine-learned models and systems? How do we protect the privacy of users when building large-scale, AI-based systems? How can we develop a rigorous understanding of the vulnerabilities inherent to machine learning? This course will discuss the mathematical concepts behind fairness, privacy, and trustworthiness of ML as well as develop the mathematical tools, metrics, and methods to mitigate them.