Summaries / Cheat Sheets created during BsC in Computer Science at ETH Zurich (2017 - 2020, semester 1 - 6) and MsC in Data Science (2020 - 2021, semester 7 - 9).
If you have any questions or comments, feel free to reach out at [email protected]. And if you are browsing this repository because you are preparing for an exam: Good luck!
- Algorithms and Data Structures (typed summary)
- Discrete Mathematics (handwritten cheat sheet)
- Linear Algebra (handwritten cheat sheet)
- Calculus 1 (typed cheat sheet)
- Design of Digital Circuits (handwritten cheat sheet)
- Parallel Programming (typed summary)
- Calculus 2 (typed cheat sheet)
- Principles of Macroeconomics (typed summary)
- Systems Programming and Computer Architecture (typed summary)
- Computer Networks (typed summary)
- Data Modelling and Databases (typed summary)
- Formal Methods and Functional Programming (typed summary)
- Introduction to Machine Learning (typed cheat sheet)
- Probability and Statistics (typed cheat sheet)
- Applied Analysis of Variance and Experimental Design (typed cheat sheet)
- Financial Market Risks (typed summary)
- High Performance Computing for Science and Engineering I (handwritten cheat sheet)
- Information Retrieval (typed summary)
- Visual Computing (handwritten cheat sheet)
- High Performance Computing for Science and Engineering 2 (handwritten cheat sheet)
- Information Theory (typed summary)
- Rigorous Software Engineering (typed summary)
- Intellectual Property: An Introduction (typed summary)
- Advanced Machine Learning (typed cheat sheet)
- Big Data (typed summary)
- Data Management Systems (typed summary)
- Fundamentals of Mathematical Statistics (typed summary)
- Cloud Computing Architecture (typed summary)
- Computational Statistics (typed cheat sheet)
Where "cheat sheet" denotes documents that we were allowed to bring to the final exam.
- BSc Thesis & Presentation
- MSc Thesis
- MSc Thesis Presentation
- Summary about a talk by Patrick Schwab for the Causal Representation Learning Seminar
- Response Essays for "Sequencing Legal DNA, NLP for Law and Political Economy"
- Response Essays for "Building a Robot Judge: Data Science for Decision-Making"