- Course description: A graduate level course introducing the basic concepts and mathematical tools that constitute the foundations of the theory of Machine Learning (ML). In particular, the course covers some theoretical aspects of learning theory (PAC learnability, VC theory), and the main ML subfields, including supervised learning (linear classification and regression, SVM, and deep learning), unsupervised learning (clustering), and active learning.
- Role: Teaching Assistant
- Course rounds: Spring 2022, Spring 2023
- Responsibilities:
- Solving numerical problems in the exercise sessions
- Creating computer laboratory exercises
- Conducting laboratory sessions
- Grading homeworks and exam