beccadsouza/eth-cs-notes

Lecture notes and cheatsheets for Master's in Computer Science at ETH Zurich

ETH Computer Science Notes

These are a portion of the notes I kept for the lectures in my Master's in ETH Zurich. They are mostly based on the primary sources (i.e. lecture slides, tutorials, recordings, exercises), but there are occasional parts borrowed from referenced or unreferenced sources (a simple web search would possibly find the source). Repository contains lecture notes and complementary cheatsheets for the courses with exams. You can also find some of the course projects I did, in their respective repositories linked below. Click on the emojis!

Course	Website	Notes	Cheatsheet	Project
Advanced Machine Learning (AML)	🌍	📚	📃
Advanced Systems Lab (ASL)	🌍			🎓
Advanced Topics in Machine Learning (ATML)	🌍			🎓
Computational Intelligence Lab (CIL)	🌍	📚	📃
Computer Vision (CV)	🌍	📚
Fairness, Explainability, and Accountability for ML (FEAML)	🌍			🎓
Machine Learning for Health Care (MLHC)	🌍	📚	📃
Machine Perception (MP)	🌍	📚	📃
Natural Language Understanding (NLU)	🌍	📚
Probabilistic Artificial Intelligence (PAI)	🌍	📚	📃
Statistical Learning Theory (SLT)	🌍	📚	📃	🎓

These notes are by no means intended to be complete or comprehensive. If you see some gaps or omitted details it is possibly because either I find the topic very general or I did not really understand it at all. Me being lazy to format it could be another possible reason. That being said, I welcome any suggestions on additional content. Similarly, there could be mistakes in the notes either because I copied and pasted parts from various sources or that I misunderstood the content. Please send me a pull request or an e-mail if I have a typo or any kind of misinformation in the notes.

Cheatsheets that were based on someone else's original work are as follows:

• AML cheatsheet is adapted from here.
• CIL cheatsheet is adapted from here, which in turn is a fork of this.
• PAI cheatsheet is adapted from this in here.