Objectives Decision trees and random forests have been used in our group for some years. It is good to have a review on its basic theory, limitation and recent developments. The study includes, but not limited to, the definition of decision trees, binary trees, multi-decision trees, ensemble methods, bagging, boosting, random forest, and applications to object recognition and super-resolution. Some attention must be given to "Randomness" theory and random trees. Please point out the significance of similarity and confidence measures. Give further examples (outside the paper(s)) of similarity and confidence measures, and the ways to achieve high confidence decision with a number of weak classifiers. Outlines IntroductionRandomness Theory (Random Forests)Confidence MeasurementApplicationsConclusion Appendix: Ensemble Learning Presentation Slides Q&A References [1] W.C. Siu, X.F. Yang, L.W. Wang, J.J. Huang and Z.S. Liu, "Introduction to Random Tree and Random Forests for Fast Signal Processing and Object Recognition", Chapter 1 of Learning Approaches in Signal Processing", Pan Stanford Series on Digital Signal Processing, Vol.2, November 2018 (Edited by W.C. Siu, L.P. Chau, L. Wang and T. Tan), November 2018.…