ETH Zürich Computer Science BsC / Data Science MsC Documents

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!

Summaries & Cheat Sheets

• 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.

Other Documents

• 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"