The only thing I'd probably add is that there's a pretty significant gap going from learning linear algebra to more advanced topics such as LDA.
For people who are just getting started with machine learning, it's probably best to get started with implementing some of the more "intuitive" algorithms such as decision trees, k-means, and naive Bayes before moving over to some of the more recent academic work.
Other things that are pretty useful, but often forgotten, such as feature selection, data normalization, and even data visualization. Algorithms are usually just one part of machine learning, but even the best algorithm wouldn't be able to do anything without identifying what the best features of your data are.
Still, it's a great list of more advanced topics, and definitely something I'll keep bookmarked for future reference.