The objective of this course is to familiarize students with technologies that support building trustworthy machine learning systems. Students will learn about attacks against machine learning models, defense techniques to mitigate such attacks, as well as interpretation methods that can make machine learning models more transparent. This course is appropriate for students who are interested in trustworthy machine learning and want to investigate key opportunities and challenges emerging in the research of this area.

I. Machine learning overview
II. Adversarial machine learning
- Data poisoning attacks
- Evasion attacks (adversarial examples)
- Defenses against poisoning attacks
- Defenses against adversarial examples
III. Privacy-preserving machine learning
- Data inference attacks
- Model inference attacks
- Privacy-preserving learning
IV. Transparency in machine learning
- Interpretability
- Interpretable models
- Model-agnostic methods
- Example-based explanations
V. Fairness of machine learning
- Bias in machine learning
- Algorithmic techniques for fairness