Teaching/Talks
Teaching
80516/80816 Causality and Machine Learning
In the past decades, interesting advances were made in machine learning, philosophy, and statistics for tackling long-standing causality problems, including how to discover causal knowledge from observational data, known as causal discovery, and how to infer the effect of interventions. Furthermore, it has recently been shown that the causal perspective may facilitate understanding and solving various machine learning / artificial intelligence problems such as transfer learning, semi-supervised learning, out-of-distribution prediction, disentanglement, and adversarial vulnerability. This course is accordingly concerned with understanding causality, learning causality from observational data, and using causality to tackle a large class of learning problems...... (this paragraph is placeholder)
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Talks
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