Self-study Curriculum

My goal is being able to leverage insights from existing research to develop (innovative) solutions for complex challenges and implement them in production-ready systems. I believe that a deep and comprehensive understanding of AI, computer science, and mathematics is essential to achieve this. My approach to building this expertise combines studying textbooks and literature with hands-on, project-based work. Below, you’ll find a list of resources I’ve used and plan to use. Please note that this list is not exhaustive and may evolve over time.

Artifical Intelligence

• Wolf, A. (2022): Machine Learning Simplified: A gentle introduction to supervised learning
• Harvard University (2020): CS50’s Introduction to Artificial Intelligence with Python (Structure and content closely based on Russell & Norvig: Artificial Intelligence: A Modern Appoach)
• Karpathy, A. (2022): Neural Networks: Zero to Hero
• Sutton, R. S. & A. G. Barto (2018): Reinforcement Learning: An Introduction (Possible accompanying course: DeepMind x UCL | Deep Learning Lecture Series)

Computer Science

• Matthes, E. (2019): Python Crash Course: A Hands-On, Project-Based Introduction to Programming
• Nisan, N. & S. Schocken (2021): The Elements of Computing Systems: Building a Modern Computer from First Principles with accompanying courses: Nand To Tetris Part I & II
• Bryant, R. E. & D. R. O’Hallaron (2015): Computer Systems: A Programmer’s Perspective (Possible accompanying course: Introduction to Computer Systems; Requires knowledge of C)
• Massachusetts Institute of Technology (2020): The Missing Semester of Your CS Education
• Chen, T. (2022): Machine Learning Compilation
• Chen, T. & Z. Kolter (2022): Deep Learning Systems: Algorithms and Implementation

Mathematics

• Epp, S. (2020): Discrete Mathematics with Applications
• Hornsby et al. (2020): Graphical Approach to Algebra & Trigonometry
• Deisenroth et al. (2020): Mathematics for Machine Learning
• James et al. (2017) An Introduction to Statistical Learning
• Bärtl, M. (2017): Statistik Schritt für Schritt

Scientific Writing

• Wördenweber, M. (2019): Leitfaden für wissenschaftliche Arbeiten
• Frank, A. et al. (2013): Schlüsselkompetenzen: Schreiben in Studium und Beruf