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.

### Computation Geometry: Algorithms and Applications - Mark de Berg [WIP]

The only book I could found about computation geometry. I find the organization of the book sometimes a bit chaotic, but at the end I could learn everything I wanted. 4/5.

### Linear Programming: Foundations and Extensions - Robert J. Vanderbei [WIP]

Very good book for linear programming without that much maths, very easy to read and lots of exercises.

# Pure Maths

### Linear Algebra - Gerd Fisher

Excellent book. Really love the exercises.

### Analysis 1 - Otto Forster

Covers everything one needs for univariate differentiation und integration.

### Linear Algebra done right - Sheldon Axler [WIP]

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 [WIP]

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

### Stochastik - Hans-Otto Georgii [WIP]

Very rigorous book about probability theory and statistics. The measure theory gave me some trouble, but nothing that Wikipedia could not help.

# Probability and statistics

### All of statistics: A concise course in statistical inference - Larry Wasserman [WIP]

Short introduction on every possible topic of statistics. Not that suitable for first time interaction with 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 from a rather philosophical aspect.

### 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.