ucinereos's site
ETH Cheatsheets
Cheatsheet and summary collection for BsC CS at ETHZ
This collection contains my summaries/cheatsheets throughout the bachelor's programme. Cheatsheets which can be used for an exam are marked with an icon, but please be sure to check the current requirements for each lecture.
Summaries and cheatsheets are written mostly with LaTeX, with the exception for coding intensive courses I prefer markdown. The site also contains the handwritten cheatsheets required for some courses.
Semester 1
Discrete Mathematics
Proof patterns, set theory, relations, functions, number theory, algebra, group theory, proof systems, propositional logic, predicate logic
Linear Algebra
Complex numbers, linear equations, vector spaces, linear images, fundamental spaces of a matrix, determinant, eigenvalues and eigenvectors, spectral decomposition, SVD
Semester 2
Algorithms and Probability
Graph theory (connection, circles, matchings, coloring), probability theory, randomized algorithms, flow networks
Analysis I
Statements from the lecture notes or script, tips and tricks, integral table, trigonometric identities, common limits, exercises
Digital Design and Computer Architecture
Boolean algebra, logic, verilog stuff, FSM, ISA an microarch, dataflow, pipelining, OoO execution, VLIW, SIMD, branch prediction, memory and caches
Parallel Programming
Java threading, hardware parallelism, races, locks, concurrency theory, lock-free programming, transactional memory, message passing, parallel algorithms, speedups, etc.
Semester 3
Analysis II
Statements from the lecture notes or script, tips and tricks, differential equations, mutlidimensional calculus, graphics of coordinate systems, trigonometric identities, integral table.
Theoretical computer science
Definitions and Theorems from the script. This is unfinished and will not be finished!
Semester 4
Data Modelling and Databases
Database types, relational model, relational calculus, SQL, entity-relationship model, functional dependencies, normal forms, analytics, bugger pool manager, access methods, operator execution, query optimization, transactions, locking, recovery, distributed database systems.
Introduction to Machine Learning
Regression and optimization, model selection, classification, hypothesis testing, kernel trick, neural networks, clustering, dimensionality reduction, probabilistic modelling, gaussian mixture models, EM algorithms, generative modeling with LLM and a few mathematical additions.
Probability and Statistics
Mathematical framework, conditional probabilities, random variables, expectation, joint distributions, convergence to expectation, conditional expectation, estimators, tests, confidence intervals, list of distributions with graph.