# References for machine learning and RCF


To learn more about machine learning and this algorithm, we suggest the following resources:
+ The article [Robust Random Cut Forest (RRCF): A No Math Explanation](https://www.linkedin.com/pulse/robust-random-cut-forest-rrcf-math-explanation-logan-wilt/) provides a lucid explanation without the mathematical equations. 
+ The book [*The Elements of Statistical Learning: Data Mining, Inference, and Prediction*, Second Edition (Springer Series in Statistics)](https://www.amazon.com/Elements-Statistical-Learning-Prediction-Statistics/dp/0387848576) provides a thorough foundation on machine learning. 
+ [http://proceedings.mlr.press/v48/guha16.pdf](http://proceedings.mlr.press/v48/guha16.pdf), a scholarly paper that dives deep into the technicalities of both anomaly detection and forecasting, with examples. 

A different approach to RCF appears in other AWS services. If you want to explore how RCF is used in other services, see the following:
+ *Amazon Managed Service for Apache Flink SQL Reference: *[RANDOM\$1CUT\$1FOREST](https://docs.aws.amazon.com/kinesisanalytics/latest/sqlref/sqlrf-random-cut-forest.html) and [RANDOM\$1CUT\$1FOREST\$1WITH\$1EXPLANATION](https://docs.aws.amazon.com/kinesisanalytics/latest/sqlref/sqlrf-random-cut-forest-with-explanation.html)
+ *Amazon SageMaker Developer Guide: *[Random Cut Forest (RCF) Algorithm](https://docs.aws.amazon.com/sagemaker/latest/dg/randomcutforest.html). This approach is also explained in [The Random Cut Forest Algorithm](https://freecontent.manning.com/the-randomcutforest-algorithm/), a chapter in [Machine Learning for Business](https://www.amazon.com/Machine-Learning-Business-Doug-Hudgeon/dp/1617295833/ref=sr_1_3) (October 2018).