Additional resources are available here:

• Breiman, L. (2001). Random Forests. Machine Learning, 45(1), 5-32.
• Chen, T., & Guestrin, C. (2016, August). Xgboost: A scalable tree boosting system. In Proceedings of the 22nd international conference on knowledge discovery and data mining (pp. 785-794). ACM.
• Freund, Y., & Schapire, R. E. (1996, July). Experiments with a new boosting algorithm. In Icml (Vol. 96, pp. 148-156).
• Freund, Y., & Schapire, R. E. (1997). A decision-theoretic generalization of on-line learning and an application to boosting. Journal of computer and system sciences, 55(1), 119-139.
• Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., ... & Liu, T. Y. (2017). Lightgbm: A highly efficient gradient boosting decision tree. In Advances in Neural Information Processing Systems (pp. 3146-3154).