Ah yes you're correct. Fibonacci heap's aren't usually used in most applications of dijkstra's algorithm (such as road networks) though because trading an O(logn) heap.decrease_key operation for an O(1) heap.decrease_key operation, but getting a slower heap.delete_min operation (by a constant factor) isn't worth it.
This is because there are much fewer heap.decrease_key operations on average than Dijkstra's worst case analysis suggests. The expected number of heap.decrease_key operations is not large enough to offset the loss in average runtime for the heap.delete_min operation.
I found that even Introduction to Statistical Learning made a few too many assumptions when I tried to work through it. I recently finished Jim Hefferon's Linear Algebra [1] and now I'm working through Introduction to Applications of Linear Algebra: Vectors, Matrices, and Least Squares [2] (along with a python companion [3]). The two texts have overlaps but I've found them more helpful than redundant; it's nice to hear different angles on the same topic. I'm planning to focus on statistics next with Blitzstein and Hwang's Introduction to Probability [4] before returning to ISLR.
> I found that even Introduction to Statistical Learning made a few too many assumptions when I tried to work through it.
Not at all surprising. From the preface:
> One of the reasons for ESL's popularity is its relatively accessible style. But ESL is intended for individuals with advanced training in the mathematical sciences.
> ... [ISL] is appropriate for advanced undergraduates or master's students in statistics or related quantitative fields or for individuals in other disciplines who wish to use statistical learning tools to analyze their data.
So by that reading, the authors simplified ESL's material from "advanced training in the mathematical sciences" down to "advanced undergraduates or master's students in statistics or related quantitative fields". I think that tells you all you need to know about how difficult ISL ESL should be expected to be.
Given that even ISL expects you to be partway through a university education in math and stats, if it's been a while or if you never studied linear algebra, statistics, or probability at that level in the first place, you won't be ready. That's probably why it irks me so much that ESL gets brought up so much as the starting point for a lot of folks. It's a good starting point for a Ph.D. from another field, but not for, like, a random software developer who's got an interest in ML. It's just setting them up for failure when the SIMPLIFIED version expects them to be partway through a relevant degree.
> I'm planning to focus on statistics next with Blitzstein and Hwang's Introduction to Probability [4] before returning to ISLR.
I think your references form a really solid sequence of prerequisites. I'll again plug what I've been plugging in a few other comments: [1]. In that one, you could probably get through ISL after the Hogg text. But yours is totally fine as well.
One other thing I'll add: I found stat110 and its companion book to focus a little too much on the "challenging" problems. It's like Blitzstein reveled in tricking you with the unintuitive parts of probability. Maybe because of his background in competition math? IDK. I like the novelty of the challenging problems, but I wish they weren't so front and center in his presentation. (I also found the whole story-proof concept a little strange.) Still, the fact that so much is online for free -- including video lectures -- makes it a great resource.
I've written a self-hosted search utility for HN threads posted by whoishiring which I'll be using in my upcoming job hunt in a month or so. It performs regex-based full-text searches of top-level comments in threads posted by whoishiring. I hope it can help someone in their hunt!
