Bernhard Schölkopf is a director at the Max Planck Institute for Intelligent Systems in Tübingen, Germany, where he heads the Department of Empirical Inference. He is a leading researcher in the machine learning community, where he is particularly active in the field of kernel methods.

He wrote the book Elements of Causal Inference: Foundations and Learning Algorithms.

Intro of Bernhard and His Recent Papers on Causality

CS 7290 Special Topics in Data Science Summer 2019 Prof. Robert Osazuwa Ness Northeastern University, Khoury College of Computer Sciences

Course Syllabus

This course targets data scientists and ML engineers familiar with probabilistic generative modeling in machine learning. Learners familiar with how to use a tensor-based framework to build a Gaussian mixture model or a variational auto-encoder will find the material grafts directly onto this modeling intuition. This course focuses on causal probabilistic modeling and structural causal models because they fit nicely into that generative model framework and its toolsets. The course also covers elements of the Neyman–Rubin causal model that are commonplace in professional machine learning settings.