# Software

## Subpagina's

## Packaging/Distribution

## Machine Learning / Applied Statistics

### Papers

- http://bestofml.com/
- https://freenode-machinelearning.github.io/Resources/ArticlesReview.html#papers

### Classification methods

**Decisions tree**: how do we determine if X is y_i, for y_i E Y? You can make a decision tree, which walks over possiblities. Usually, these trees are binary. You can use the tree to test new (partial) datapoints: based on old data, which made you construct the tree a certain way, how would the new datapoint be classified? link visialize decision trees**Random forests**build upon decision trees, and combine multiple decision trees, which should be as uncorrelated as possible. The idea is multiple (weak) classifiers combined might produce one (better) classifier. link1, link2**Isolation forests**: how distant is a point from other points? Or: how many random cuts does it take to isolate a point from the dist? Large distances/large nb of cuts should find outliers.

### Tutorials / writeups

## tech/stats

- https://root.cern.ch/roofit-20-minutes
- https://ghidra-sre.org/
- http://quantumalgorithmzoo.org/
- https://github.com/JuliaDiffEq/ModelingToolkit.jl
- xtensor
- bqplot
- https://machinekoder.com/how-to-not-shoot-yourself-in-the-foot-using-python-qt/
- Kalman filters: predict what a changing system will do next.
- setup fpc in vscode
- Reed Solomon codes