Papers that I like. I won't lose them here.
These are a series of papers on ML, AI and Data that I find useful.
Here are some web sites that students like as well.
https://towardsdatascience.com/top-sources-for-machine-learning-datasets-bb6d0dc3378b - open source datasets
https://seeing-theory.brown.edu/ - visualization of statistics
http://www.sandia.gov/~wpk/slides/avatar-ensembles.pdf Decision trees and ensembles
https://www.mdpi.com/2079-9292/8/3/292/pdf-vor Nice recent overview of some of the state of the field
https://www.naftaliharris.com/blog/visualizing-dbscan-clustering/ - visualization of the DBSCAN algorithm
https://jeremykun.com/main-content/ - Multiple blog posts giving background mathematical motivation to various ML and AI concepts. Examples in python.
https://github.com/scikit-learn-contrib/imbalanced-learn - A python library to offer lots of algorithms to resample inbalanced datasets.
https://colah.github.io/posts/2014-03-NN-Manifolds-Topology/ - Nice visual explanation of how the neural network nonlinearity works.
https://towardsdatascience.com/the-complete-reinforcement-learning-dictionary-e16230b7d24e - Reinforcement Learning vocabulary