Contains implementations for some of the fundamental machine learning techniques.

1. k-nearest neighbours and linear regression
2. SVD and cholesky factorization for generalized linear models
3. gradient descent and logistics regression
4. Bayesian logistics regression with Monte Carlo methods
5. neural network for digit classification