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Beside of visiting the lectures and doing the homework carefully, students can profit greatly from self-studying outside of university. Not only the concepts will be explained different from how the professors' teaching, therefore giving students new point of view; the literatures also regularly reveal how the learning subject can be applied in advanced courses, hence play a motivational role.

Followings are some of literature recommendation, which I heaviliy utilised for my switch from an practical engineering studying field to a more theoretical Computer Science. After struggling a lots with finding books on the right diffculty spectrum, I constructed the following most important criteria a trustworthy textbook has to meet:

• Beginner friendly: theoretical computer science can sometimes be very hard, beginners will only face more challenges when a proof skips many intermediate steps to the final conclusion. Therefore I prefer authors who do not make any assumption of readers' background.

• Proof oriented: while concrete examples are a important, practicing to construct proofs are the only way to learn and understand concepts effectively.

• Includes solutions or solutions can be found online: this is one point where people’s opinions split. My rule of thumb is to try to solve the exercises myself inside a certain timeframe, after stucking on a step long enough, I take a look at the solution for hint. It would be otherwise very difficult for me to know, if my solution is even correct.

### Tutorium Analysis 1 und Lineare Algebra 1 - Florian Modler

This book is meant to accompany math major students for one semester, the theorems and corresponding proofs are essential and therefore perfect for self-studying. However, no exercises are included so I could not rely only on this book.

### Analysis 1 / Übungsbuch zur Analysis 1 - Otto Forster

This bundle is the opposite of Florian Modler’s lecture, consists mostly of exercises with solutions. A perfect book to prepare for the final exam.

### Lineare Algebra | Eine Einführung in die Wissenschaft der Vektoren, Abbildungen und Matrizen - Albrecht Beutelspacher

Usually when it comes to Linear Algebra, the beginners would get recommended Gilbert Strang’s “Introduction to linear algebra”. I personally find Strang too talkative and his approach not very rigorous enough for university’s math level. Beutelspacher on the other hand gives the students a very gentle introduction for proof-heavy linear algebra with vector space as main focus, which I find easy enough for learning on myself, but still challenging enough to struggle on the usual basis.

### Lectures on probability theory and mathematical statistics - Marco Taboga

A very detailed book on probability theory and statistics. When it comes to probability for computer science, this one is the best I have every tried (and I have tried many, before). Taboga leaves no stone of probability unturned, important concepts are explained very well and understandable. Students will get a foundation of set arithmetric, random variable, characteristics values, probability distributions up to statistical inference, hypothesis test and variance analysis.

### Statistik der Weg zur Datenanalyse - Ludwig Fahrmeier

This one is a rather practical book on statistics with a focus on connecting theoretical concepts and applying them for problem solving. A probability theory course may gave me the theoretical foundation, but it was this book which made me fall in love with statistics and data driven thinking.