Favorite Computer Science literature

2021-01-18
2 min read

List will be updated regularly.

Algorithms and complexity

Introduction to Algorithms, third edition - Thomas H. Cormen

Tons of proofs, exercises and graphics. THE book to learn algorithms. A bit maths heavy but nothing wild.

Algorithm Design: Pearson New International Ed - J. Kleinberg

This book takes more time and space to explain topics than Introduction to Algorithms. Contains fewer maths heavy proofs and more practical applications.

Introduction to Graph Theory Taschenbuch - Richard J Trudeau

A short pure maths book for non-mathematicians. I loved every bit of this book and its exercises.

Pure Maths

Linear algebra done right - Sheldon Axler

This book is not for people with little time. And definitively not for people without any experience with linear algebra.

Principles of Mathematical Analysis - Walter Rudin

Seriously, this book (also known as Baby Rudin) kicks ass. I don’t even want to know how big Rudin would be.

Probability and statistics

Lectures on probability theory and mathematical statistics - Marco Taboga

A very detailed book on probability theory and statistics with a good mixture of proofs and exercises. A perfect book as reference since every chapter was written independent.

Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan - John Kruschke

Excellent introduction into bayesian statistics without too much mathematics.

An Introduction to Generalized Linear Models - Annette J. Dobson

Probably the best book on GLM. Heavy maths and very many exercises.

Statistik der Weg zur Datenanalyse - Ludwig Fahrmeier

A practical book on statistics with a focus on connecting theoretical concepts and applying them for problem solving.

Einf├╝hrung in die Wahrscheinlichkeitstheorie und Statistik (vieweg studium; Aufbaukurs Mathematik) (German Edition)

Short and rigorous introduction to probability theory without any measure theory.