# Various machine learning algorithms. Mostly vanilla.

* [bayesian optimization](https://www.ritchievink.com/blog/2019/08/25/algorithm-breakdown-bayesian-optimization/)
* [gaussian processes](https://www.ritchievink.com/blog/2019/02/01/an-intuitive-introduction-to-gaussian-processes/)
* [bayesian generalized additive models](https://www.ritchievink.com/blog/2018/10/09/build-facebooks-prophet-in-pymc3-bayesian-time-series-analyis-with-generalized-additive-models/)
* [gradient boosting machines](https://www.ritchievink.com/blog/2018/10/09/algorithm-breakdown-why-do-we-call-it-gradient-boosting/)
* [dirichlet gaussian mixture models](https://www.ritchievink.com/blog/2018/06/05/clustering-data-with-dirichlet-mixtures-in-edward-and-pymc3/)
* [affinity propagation](https://www.ritchievink.com/blog/2018/05/18/algorithm-breakdown-affinity-propagation/)
* [genetic algorithms](https://www.ritchievink.com/blog/2018/01/14/computer-build-me-a-bridge/)
* [support vector machine](https://www.ritchievink.com/blog/2017/11/27/implementing-a-support-vector-machine-in-scala/)
* [neural network](https://ritchievink.com/blog/2017/07/10/programming-a-neural-network-from-scratch/)
* [arima models](https://www.ritchievink.com/blog/2018/09/26/algorithm-breakdown-ar-ma-and-arima-models./)
* [expectation maximization](/clustering/expectation_maximization.ipynb)