Computer Scientists’ Cheatsheet¶
This documentation is a public note of any topic regards computer science. May it be Machine Learning, Discrete Mathematics or Bayesian Data Analysis.
These docs are mainly maintained by Seyoung Park.
Online courses¶
Contents¶
- AI
- Bayesian
- Bayesian – Fundamentals
- Bayesian Network
- Bayesian Models
- Markov Chain Monte Carlo
- Probabilistic modeling
- Variational Inference
- Practices
- Q. Conditional independence from Bayesian network
- Q. Burden of specification
- Q. DAG representation
- Q. Derivation of the posterior distribution
- Q. Multivariate Gaussian
- Q3. Wishart distribution
- Q. Posterior of regression weights
- Q. Poisson regression with Laplace approximation
- Q. Variational approximation for a simple distribution
- References
- Calculus
- Computer Graphics
- Deep Learning
- Information visualization
- Linear Algebra
- Machine Learning
- Papers
- Probabilities and Statistics
- Python
- UNIX
Metadocs¶
These docs are open source: all content is licensed under CC-BY 4.0 and all examples under CC0 (public domain). Additionally, this is an open project and we strongly encourage anyone to contribute. For information, see the About these docs and the Github links at the top of every page.