Ask HN: How to best acquire theoretical computer science knowledge?

Start with a course on computer architecture / digital design. Something that'll have you put together a simple 8 bit CPU. Move your way up with a course on operating systems - processes, swapping, cache invalidation, file systems, drivers.

Then, maybe something on compilers? Or how to write your own interpreter.

Finally, a good networking course is pretty important.

Very little of this is theoretical compsci, but it's something that will get covered in a degree program. That said, knowing how to recognize what kind of class of algorithm we're looking at, and data structures all around.

Read lots of papers. Start with easy ones to learn how to best digest highly academic technical writing then keep pushing.

CS is remarkable in how much of its academic knowledge is online, available, for free. Taking advantage of that resource is beyond key.

If you know roughly what you want to learn, search out a seminal paper in that area. If it's deep CS, you'll probably find a good one from the 80s. Seek out every citation that's proximal to a sentence in that paper that confuses or excites you. Search on Google Scholar for all the recent papers that cite the one you found and begin eating up the chain that direction. When you find a deep topic you want to learn more of—set theory, logic, formal languages, discrete math, matrix analysis, automata theory, geometry, topology, &c.—don't be afraid to look for a good book on the topic. There are resources around the internet answering "What's a good introductory book to X" for all kinds of X.

If you don't know the general area you're interested in then I recommend the exact same strategy but with greater emphasis on following citations that make connections to other areas of CS. Additionally, pick a few disjoint topics (Category theory, system architecture, graph theory) and follow them all simultaneously looking for connections.

Formal languages, automata, and process algebras form a really fundamental mathematical course of study that I'd highly recommend to anyone interested in "why" CS works. Optimization, matrix analysis/linear algebra, and diffeq form a great basis for simulation and machine learning.

Generally, for any of these routes, learn to find your boundary of knowledge. Studying source material is very challenging because any given paper will tend to assume many things about your background. Most of those assumptions will be false and lead to density of the paper. You can often power through without them, but you can also take it as an indication of a new place to study.

Finally, see if you can find a study group or journal club. I'm not there, so I don't have firsthand experience, but I'm sure there are many in the bay area.

(Oh, and if you read mathematical papers/books don't skip the proofs and exercises. That'd be like reading the iOS documentation without ever programming anything.)

Education will not teach you anything, but it will give a great opportunity to learn. Many will simply learn enough to get through exams and then promtly forget everything again.

You will also get introduced to concepts and areas you would never even think of exploring otherwise. I thought graph theory sounded boring, but now I enjoy it. Formal languages and automata theory? Physics? Electronics? Hardware design? These are topics I would never have explored if it wasn't for going to university.

Artificial intelligence, compilers and operating systems are topics I probably would have explored, but I sincerely doubt I would go as deep as I have.

Also do not underestimate the effect deadlines can have. Procrastination is a constant evil in my life somewhat negated by school forcing me to do things.

* Sedgewick has no proofs in his book (or at least very few). CLRS has a lot of proofs in it.

* CLRS uses pseudocode, while Sedgewick uses actual code. I think they use Java now (it was Pascal in the edition that I had used).

* Sedgewick is really a practitioners introduction. It views the algorithmic problem as the jumping off point. Whereas CLRS is more an intro for people who are going to study computer science -- so it focuses on the methods.

Both good texts. I think for most programmers I'd recommend Sedgewick, but if your goal is to be a computer scientist, then I'd recommend CLRS.