layout: distill title: Notes About Books description: Notes about books I came across and added to my reading list. date: 2021-10-24
authors:
bibliography: 2021-10-24_books.bib
ToDo: machine learning notes book, bishop, theory book
Graduate level introduction to graph theory for computer scientists and mathematicians. A really awesome book. It requires nearly no prerequisit knowledge of mathematics, gives rigouros definitions of all graph theory concepts relevant to a graph learning researcher and shows some deeper theorems connecting graph theory concepts, which allows you to really crasp the gravity of these concepts and learns you how to rigorously do graph theoretic arguments.
In the beginning of the book on its content page, some subchapters are marked with stars “*” meaning that they are recommended for a first course in graph theory. I followed these recommendations and was not disappointed ;).
Link to the books official (full-text) preview: click
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.
by Robert G. Bartle
Use: In 100 pages gives a gentle introduction into the lebesque integral and basic measure theory so as to read probability and statistics texts formulated in the language of measure theory instead of probability.
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