Both the intro and this one are great reference books but I don't find them suitable to study as the main textbook. They cover a large number of topics so the depth of each topic is pretty limited. Keep in mind if you are considering to study these.
I would even go further and say I did not find the intro book a good book at all.
The text was full of non-trivial errors that genuinely hindered students' understanding. Moreover, the presentation was not particularly enlightening -- lengthy mathematical discussions therein were not neither rigorous enough for a proper mathematical introduction; nor distilled enough for an application practitioners. I understand that Murphy explicitly tried to strike a balance -- I wonder if this balance ended up being in the awkward no man's land.
I do agree that I found the book better as a secondary reference due to its breadth of topics. The second book seems to continue this trend of covering even more topics.
>The text was full of non-trivial errors that genuinely hindered students' understanding
Kudos to the author for putting out a free version and for the work but the number of errors seems crazy high (I checked a couple and doesn't seem like they were fixed in the 2023-06-21 draft pdf he has put on his website), I have the 2022 book so definitely have to look into the error list.
Ironically I found it to be too deep. I want a quick feel for the mathematical structure and ergonomics of a field before really diving into 400 pages on logistic regressions.
With a similar probability focus, Pattern Recognition and Machine Learning by Christopher Bishop [1] is pretty good. If you are looking into deep learning specifically, I think François Chollet's Deep Learning with Python is one of the most accessible books.
I think "Understanding Deep Learning" is very nice - https://udlbook.github.io/udlbook/ (an covers almost all topics, it has maybe just a couple of omissions, such as Multimodal Learning, NERFs and Time Series Prediction)
