Tutorials that explain boosting I'm a newbie trying to learn Boosting. The examples I found online are quite confusing. Is there a simple tutorial somewhere that explains Boosting with an example?
 A: Recommended in comments:
A short note on boosting in the context of decision trees is provided in James et al. "An Introduction to Statistical Learning" p. 321-324. More detailed treatment is in Hastie et al. "The Elements of Statistical Learning" Chapter 10. – Richard Hardy 
A working guide to boosted regression trees. Journal of Animal Ecology often used in introductory courses – charles
A: Machine Learning: Classification
This MOOC on Coursera by University of Washington has a very good and comprehensive explanation of boosting models in Week 5. They have specifically focused on Adaboost and have given a very good and easy to understand explanation of the model and the mathematics behind it. To get a glimpse of how the video is you can look at this pdf 
I also found this article to be a very good intuitive explanation of XGBoost.
A: Chris' Bishop Pattern Recognition and Machine Learning has a full chapter ( Chapter 14) which addresses Bagging, boosting etc, def. worth a look, hope this helps! 
