The previous discussion is about the version dated November 4th, 2014. The book referenced here is dated January 4th, 2018. From looking at the TOCs, most chapters contain a few additional sections and there is an entirely new chapter. This might warrant a second discussion.

I'd avoid that site- from our Cisco proxy:

"Based on your organization's access policies, this web site ( http://drona.csa.iisc.ernet.in/~chiru/datascience/iisclectur... ) has been blocked because it has been determined by Web Reputation Filters to be a security threat to your computer or the organization's network. This web site has been associated with malware/spyware."

Logistic regression is also referred to as a "supervised classification" problem, which this book only addresses in the specialized space of document clustering or image classification. They do also address Support Vector Machines, which is a generalized algorithm for classification. However, there are a wide variety of specific implementations of logistic regressions that require quite a bit more conversation (dummy variables, log-odds ratios, ordinal variables) that are more directly applicable to a general stats background to machine learning itself.

Considering that the authors are all CS professors or researchers and not statisticians, that makes sense to me why they don't view logistic regression as foundational.

No,it's just linear regression. You'll find it discussed in a stats textbook since its relatively old (and as the joke goes, data science is what a statistician calls themselves when they want a pay rise).

That being said, IMO it's one of the most over-applied techniques around, and it gives many non-technical and technical users the illusion that they understand what's going on in the model...

https://mitpress.mit.edu/books/machine-learning-1 is great as well.

Any idea of what material overlaps or which to read first?