# Notes

### Machine learning

- Yet another introduction to backpropagation
- Yet another introduction to probabilistic graphical models
- Gibbs sampling for fitting finite and infinite Gaussian mixture models [code]
- Vector and matrix calculus
- Dynamic programming
- Principal components analysis
- Autoencoder, variational AE, vector-quantised VAE and categorical VAE