Self-study Curriculum

My goal is to be able to leverage insights from existing research to develop innovative solutions for complex challenges and transform them into production-ready systems that deliver real-world value. I believe that a deep and comprehensive understanding of AI, computer science, and mathematics is essential to achieving 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 have 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 Artificial Intelligence: A Modern Appoach by Russell & Norvig.
• Karpathy, A. (2022). Neural Networks: Zero to Hero.
• Sutton, R. S. & Barto, A. G. (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. & Schocken, S. (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. & O’Hallaron, D. R. (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. & Kolter, Z. (2022). Deep Learning Systems: Algorithms and Implementation.

Mathematics

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

Science & Writing

• Schimel, J. (2012). Writing Science: How to Write Papers That Get Cited and Proposals That Get Funded.
• Wördenweber, M. (2019). Leitfaden für wissenschaftliche Arbeiten.
• Frank, A. et al. (2013). Schlüsselkompetenzen: Schreiben in Studium und Beruf.