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 https://uni-tuebingen.de/en/180804.
Contents:
* 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.
