Probabilistic ML – Lecture 14 – Generalized Linear Models

This is the fourteenth lecture in the Probabilistic ML class of Prof. Dr. Philipp Hennig in the Summer Term 2020 at the University of Tübingen.

Time-stamped slides available at

* Laplace Approximations
* Support Vector Machines (SVMs)
* Generalized Linear Models
* Bayesian Deep Learning
* ReLU Networks

© Philipp Hennig / University of Tübingen, 2020 CC BY-NC-SA 3.0

Known bugs: On slide 8, the Hessian should read y^2 / sigma^2 (with a square at y). This arises from a missed chain-rule term in the derivation on the whiteboard.

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