Introduction to modern statistical machine learning, in particular probabilistic models.
Syllabus:
• Hands-on Introduction
• Bayesian Decision Theory, Generative Probabilistic Models
• Discriminative Probabilistic Models
• Maximum Margin Classifiers, Generalized Linear Models
• Estimators; Overfitting/Underfitting, Regularization, Model Selection
• Bias/Fairness, Domain Adaptation
• Statistical Learning Theory
• Deep Learning
- Teacher: Christoph Lampert
- Teaching Assistant: Jonathan Scott