layout: distill title: Notes About Books description: A list about interesting talks and lectures about machine learning, deep learning and graph learning I came across. date: 2021-11-08

authors:

• name: Lukas Gosch url: “https://saper0.github.io/” affiliations: name: Data Mining and Machine Learning, TU Munich

bibliography: 2021-10-24_books.bib

Graph Learning

ToDo: lecture machine learning with graphs

Matthias Fey, the creator of PyTorch Geometric (PyG) gave a very cool presentation of PyG 2.0 at the Graph Representation Learning Reading Group at Mila - Quebec AI Institue. Direct link to the talk here.

Graph Theory

Probability Theory

Basic Probability Theory

by Robert B. Ash

Use: Second introduction to probability theory. Introduces wordings of measure theory but does not built on it. Proofs basic combinatoric formulars in chapter 1.4 and Stirling’s Formula in 1.8. Maybe interesting to read the Characteristic Functions Chapter, which also includes a proof of the central limit theorem.

Philosophy & Ethics

Philosophy of Technology

by Maarten Frannsen, Gert-Jan Lokhorst and Ibo van de Poel

Used in related a TU Munich course as textbook and online in The Stanford Encyclopedia of Philosophy