Add reference links

[dkirkby]

Add useful links here that should be included in the notebooks...

[dkirkby]

http://www.r2d3.us/visual-intro-to-machine-learning-part-1/ has great visualizations of the general ML setup and concepts and decision trees in particular.

[dkirkby]

Simpson's paradox https://pwacker.com/simpson.html

[dkirkby]

Humorous video related to gradient descent (or, equally, grad student desecent): https://www.youtube.com/watch?v=cvuktushEhY

[dkirkby]

Review these lecture notes on ML for Physicists: https://sites.krieger.jhu.edu/jared-kaplan/files/2019/04/ContemporaryMLforPhysicists.pdf

[dkirkby]

Recorded seminars:

• LBL DL4Sci 2019 school: https://www.youtube.com/playlist?list=PL20S5EeApOSvfvEyhCPOUzU7zkBcR5-eL
• LBL DL4Sci 2020 school:
  • https://dl4sci-school.lbl.gov/agenda
  • https://www.youtube.com/playlist?list=PL20S5EeApOSuFC7PhHtCWtCsqpNKXM64Q
• ML Club: https://docs.google.com/document/u/1/d/1GGtE-YIuAWlmpKSr38_kyiF-Fklszhkh4FkiYWzBAho/pub

[dkirkby]

Deep double descent:

• https://openai.com/blog/deep-double-descent/
• https://arxiv.org/abs/1912.02292

[dkirkby]

Google ML Crash Course https://developers.google.com/machine-learning/crash-course/ml-intro