I've implemented the following from your list in MATLAB/Octave from scratch (except where mentioned):
1) Decision Trees for classification based on entropy modelling node impurities and finding homogeneous groups, called CART set of algorithms (in R for machine learning applications)
2) Random forests with groups of these trees and using bootstrapping
3) Function to perform bootstrapping on 1D data
4) Implement ensemble by training new attributes based of previously leftovers, that's AdaBoost is good example, used in supervised image classification system project to be specific in 2016 using Viola Jones Boosting (MATLAB, Python, both with opencv)
5) Bagging with comparison to boosting for weights, and viewing results for same classifier
Most of them were done as part of university assignments, and few as independent research projects. I'd be happy to know your requirements here.
Regarding your project:
The BID is of 7days (6 working, 1 review) and $200 (approx $180 + freelancer tax), with hopefully reusing code done in one section for another. We shall divide the work into 2 milestones, details we can discuss later (if at all)
About me:
Being a research assistant in machine learning applications in image processing and in general solving classification tasks, I take up projects in those areas. I use tools as mentioned above. I've been so for 4 years by now.
Kindly message me your brief in a document or discuss it as you prefer. If I'm not online, leave your queries, attachments etc. and I shall reply ASAP.
Thank you!
Have a great weekend.