For anyone who wants to dig a bit deeper: PRML by Bishop [0].
An amazing work as a general introduction to machine learning and Bayesian in general.
MacKay's book [1] is a bit more opinionated regarding Bayesian methods.
Even if you do not become purely Bayesian, it advances your understanding of to approach data quiet a bit compared to the generic "oh data, lets throw a NN at it".<p>[0] <a href="https://www.microsoft.com/en-us/research/people/cmbishop/prml-book/" rel="nofollow">https://www.microsoft.com/en-us/research/people/cmbishop/prm...</a>
[1] <a href="http://www.inference.org.uk/mackay/itila/book.html" rel="nofollow">http://www.inference.org.uk/mackay/itila/book.html</a>
About 40 years ago I was reading a business mathematics book and ran across a section that, as I recall, started out with a basic probabilities equation and derived the basic Bayesian equation in one step.<p>Am I remembering this right? If so could someone post it?
I would really like to read the full paper for reason #3, but it’s paywalled. The whole article feels like a big teaser with the real content locked away.