This is just a collection of some small ML projects in R and Python that I did during my Master's.
Among others it includes a quick application of hierarchical clustering and k-means clustering, as well as kernel k-means. In MLP vs RF I used Bayesian Optimisation to tune the Hyper parameters of the Neural Network. The third one compares kernel ridge regression with a Gaussian process and a quick discussion of some common GP kernels.