On this page, I collect all the resources that have fascinated me throughout the years and have brought me further.

YouTube channels - Computer Science - Beginners
  • NLogSpace - In my opinion, this is the best german-language channel for an introduction to theoretical computer science.
  • MIT OpenCourseWare - Computer Science - High-quality educational videos on a wide range of computer science topics. Especially Erik Demaine is great!
  • code〈metas〉 - New YouTube channel of a very good friend of mine for getting started with algorithms, data structures, and programming $($mainly C++$)$ with a particular focus on algorithmic thinking $($e.g., when is which data structure suitable?$)$.
  • ACM A.M. Turing Award Laureate Interviews - Inspiring series with interviews of various turing award winners. The turing award is often referred to as the nobel prize of computer science. The interviews highlight the lives of the researchers, from experiences growing up to key moments in their scientific careers.

YouTube channels - Mathematics - Beginners
YouTube channels - Computer Science - Advanced
Selection of fantastic online courses
Selection of interesting reads
  • Von der Turingmaschine zum Quantencomputer – ein Gang durch die Geschichte der Komplexitätstheorie by Johannes Köbler and Olaf Beyersdorff. For German speakers, in my opinion, a must-read about the development of complexity theory from the 1930s to the present day.
  • Quantum Computing since Democritus by Scott Aaronson. An excellent book, with Scott Aaronson giving a high-level explanation of complex subjects in a clear and amusing kind of way. The first 8 chapters offer many exciting connections for any theoretical computer science enthusiast. If you want to dive into the world of quantum computing, you will find what you are looking for in this book without having to study physics or, more precisely, quantum mechanics $($but you will need linear algebra and probability theory$)$.
  • NP-complete Problems and Physical Reality by Scott Aaronson. A super interesting survey about different theoretical computational models $($e.g., time travel computing$)$ and whether they are capable of solving NP-hard problems in polynomial time.
  • Combinatorics, complexity, and randomness by Richard M. Karp. This turing award lecture by Richard Karp is on the historical development of combinatorial optimization and complexity theory. Especially the chapter randomized algorithms contains a great illustration of the power of randomness using an adversary that "plays" against a deterministic algorithm and a randomized algorithm.
  • What is Research? by Manuel Blum. Extremely valuable tips for successful research. Be a turing machine and not a finite automaton!
  • Advice on good research practices. Collection of various helpful research practices.
  • Stochastik-Formeln mit konkreten Beispielen. Collection of various helpful combinatorial formulas.
If you know of any great learning resources you don't see on this list, don't hesitate to write me! :)