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We will explain Ridge, Lasso and a Bayesian interpretation of both.
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[1] Graphing calculator to plot nice charts: https://www.desmos.com
[2] Refer section 6.2 on “Shrinkage Methods” for mathematical details: https://hastie.su.domains/ISLR2/ISLRv2_website.pdf
[3] Karush–Kuhn–Tucker conditions for constrained optimization with inequality constraints: https://en.wikipedia.org/wiki/Karush–Kuhn–Tucker_conditions
[4] stat exchange discussions on [3]: https://stats.stackexchange.com/questions/90648/kkt-versus-unconstrained-formulation-of-lasso-regression
[5] Proof of ridge regression: https://stats.stackexchange.com/questions/348494/the-proof-of-equivalent-formulas-of-ridge-regression
[6] Laplace distribution (or double exponential distribution) used for lasso prior: https://en.wikipedia.org/wiki/Laplace_distribution
[7] @ritvikmath ‘s amazing video for the bayesian interpretation of lasso and ridge regression: https://www.youtube.com/watch?v=Z6HGJMUakmc
[8] Distinction between Maximum “Likelihood” Estimations and Maximum “A Posteriori” Estimations: https://agustinus.kristia.de/techblog/2017/01/01/mle-vs-map/
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