Machine learning

Notes on machine learning.

Data

Explainabilty

Drift

Time-series

Clustering

Fairness

Metrics

Error metrics

Performance

Transformations

Optimisation

RNN

Statistics

Model selection

Kernel functions

Kernels can be interpreted as “similarity functions”. Typically they are functions that take two $n$-dimensional points and produce a scalar. In the following sections we look at some well-known kernels and their typical applications.

Unsupervised methods

Supervised methods

Methods pertaining to Supervised learning.

Techniques such as:

Regression

Frameworks

Theory