>>12244877>computabilitySoare's Turing Computability
>systems and OSMIT, Berkeley, and Stanford teacher webpages for computer architecture, systems, and OS.
Do pintOS and design your own baby virtual disk policy. Computer systems: A programmer's perspective is really good as a reference
>complexity theoryArora barak's complexity theory
>algorithmsCLRS is classic for a reason, easy to start, hard to finish. teacher webpages from MIT help a lot here too, but you won't get into super hard stuff with basic or even advanced algo until you pick a subfield of it.
>randomized algorithmsfirst you probably want to go through
http://wwwusers.di.uniroma1.it/~ale/Papers/master.pdfIt doesn't assume you're acquainted with measure, and slowly eases you into its uses at large. It's a *wonderful* text that compiles a lot of results from analysis and probability theory that you would have had to go searching for otherwise. I do recommend learning how to construct the Lebesgue measure, not to mention learning how rigorous probability measures work, but that's up to your own understanding.
>MLhttps://cs.nyu.edu/~mohri/mlbook/Very good book, doesn't take a lot of math to start but a more than decent amount of maturity to get through. The ML theory you learn here is, well, theory, but you should be able to naturally implement these results into ideas. There are more project oriented ML courses out there from the aforementioned universities - feel free to cross reference with this book.
>Functional analysisFunnily enough functional analysis is starting to get popular in algorithms groups because of dimensionality reduction, so I recommend Reed/Simon or whoever else you already know.
>graphicsCMU has excellent courses on this online free. Be sure to read up on your differential geometry.
>signals and communication taught more in EE undergrad, but studied in CS nonetheless in ML, compression, image proc., etc etc.. Use a good online course in EECS
By no means is this
