Hey! I’m a coauthor with this guy :^). Incredibly fascinating (and extremely smart) dude. His interest in origami is what drew me to MIT in the first place, since I was interested in it as well from a young age. A few years ago I took his class on the computability/complexity theory of folding, and we ended up solving an open problem in the field and published a paper out of it [0]. If you had told highschool me something like that would happen, I never would have believed it :)

[0]: https://arxiv.org/abs/1901.08564

Got confused by the broken link too. Those aren't separate links. It's a single link, and given the name of the file is "poster-design.pdf", I don't think the PDF is very important. It's just an image that gives examples of what is probably covered. The real content is described below as the video lectures and notes of the class[1] and the github account[2]. There are also problem sets[3], and it recommends a couple of books[4][5], both of which are apparently free.

[1] https://courses.csail.mit.edu/6.851/fall17/lectures/

[2] https://github.com/6851-2017

[3] https://courses.csail.mit.edu/6.851/fall17/psets/

[4] http://epubs.siam.org/doi/book/10.1137/1.9781611970265

[5] http://opendatastructures.org/

As a busy self-learner, when is it time to learn data structures in depth? I feel like there are always 10 other technologies I need to know more urgently (eg, more bash, Linux, testing frameworks, deep learning, c++ libraries, linear algebra, common security mistakes/attacks, OpenGL, etc)

the professor is a fascinating guy:

http://erikdemaine.org/

https://en.wikipedia.org/wiki/Erik_Demaine

EDIT: so is the coauthor! http://joebergeron.io/posts/post_four.html

FYI: lecture notes are here https://courses.csail.mit.edu/6.851/fall17/lectures/

Years ago, I watched all those video preparing for a Google interview. They are really great. I was told advanced data structures would be a big part of it. Not a single thing came up in the process :/

22 recorded classes

https://ocw.mit.edu/courses/electrical-engineering-and-compu...

I was pleasantly surprised to see that improving Wikipedia articles is an acceptable final project for this course. Is this an MIT thing?

I watched these lectures a few years ago and they were really good.

Since this is meant to be a graduate level research course, I wonder if any cutting edge topics have been added since? Were there any major advances or new ideas in fundamental data structures in the past few years?

“Grading...There are four requirements:”

And then it lists five requirements...do I get the MIT course description equivalent of a Knuth check now?

I happened to interact with Erik Demaine via an open source project(KaTex). It was my first OSS contribution and he was very welcoming and friendly. Of course, I was also humbled to read about his work.

I am getting 404 when I click on linked images.

The data structures look really cool, but I couldn't image example to make use of them in "everyday" software. Would someone give me some example of problems that would be easier to solve with special data structures?

At my college, this class was the primary weeding-out class in junior year. Started with a full class, maybe 50? At the end of the semester there were 7 of us.

These are some of my favorite lectures, although often goes over my head. Whenever I think I've done something clever, I watch these to ground me.

Bookmarked for when I master basic data structures.

This works: https://courses.csail.mit.edu/6.851/fall17/lectures/

Links are broken :/

Honest question, I'm very new to this but I really want to learn. Does putting the video on repeat help with grasping the concepts?

Is the instructor using Hagoromo Fulltouch Chalk?

Having been a double major at MIT I still get anxiety spikes whenever I look at one of these course webpages.

Looks like a great course though!

Guess this might help,

https://pastebin.com/m7q1WRcw

Love that they recommend going to Tarjan -- nothing like drinking from the original well!

link to pdfs 404

All time. Everyday. You'll know you understand it when you can build it, when you can see its structure.

"Smart data structures and dumb code works a lot better than the other way around." -- Guy Steele

"The Representation Principle: Once a problem is described using an appropriate representation, the problem is almost solved." -- P. H. Winston

"Show me your [code] and conceal your [data structures], and I shall continue to be mystified. Show me your [data structures], and I won't usually need your [code]; it'll be obvious." -- Fred Brooks

"I will, in fact, claim that the difference between a bad programmer and a good one is whether he considers his code or his data structures more important. Bad programmers worry about the code. Good programmers worry about data structures and their relationships." -- Linus Torvalds

"Rule 5: Data dominates. If you've chosen the right data structures and organized things well, the algorithms will almost always be self-evident. Data structures, not algorithms, are central to programming." -- Rob Pike

"What are tensors? The facts of the universe." -- Lillian Lieber

Same here. I had the same problem especially when dealing with problems that required me to go back and (un)learn stuff from scratch.

I found that the greatest barrier to deal with seemingly hard concepts was getting the right intuition on what "might" be happening under the hood. For me, this is absolutely crucial even if my intuition about a "thing" might not be completely correct.

As someone who didn't go to a classroom to be taught a lot of the basics, I dive in head-first to read papers, I mean a lot of papers, to help build this mental model EVEN though I don't understand everything in it, but it lets you pick up patterns for solving similar problems you might be having.

For things you don't understand, you can always fit in these gaps in your mental model as you dig deeper into the abstraction cake and repeat the whole "intuition" building loop.

I find that the same also applies to Math and other domains which build on a bunch of primitives that has to be really well known to be able to reason in higher abstractions.

I feel software is no different... dig deep enough, there will be a hashmap of some sort.

It depends if you're learning for pleasure or for your career. ;) When I bust out my algorithms/data structures books, I find myself engrossed much like I imagine people get with a good novel. So many times I say to myself, "Whoah! That's so cool!" because of how elegantly/efficiently a problem can be solved that I had never even considered.

My favorite so far is Skiena's "The Algorithm Design Manual". I especially love the computational geometry section.

If you're learning these topics for purposes of career advancement, probably there is not much reason to go much beyond the fundamentals that you might study for a FAANG-type interview. If you know that much, you can probably recognize a situation where you're missing something and go look for it.

Even that is probably overkill for a lot of people, practically speaking.

On the other hand, if you're learning for fun, you should study the thing you're most excited about because you will do the most interesting things with knowledge and skills that you're passionate about.

Ultimately, in my personal experience, the latter course has also led to career advancement.

> As a busy self-learner, when is it time to learn data structures in depth?

From a practical perspective... The best sign is if you're spending a lot of time trying to wrangle performance or scalability and most of the bottleneck is inside the standard data structures.

E.g. if you're spending too much time or memory doing dictionary lookups, then it may be worth looking into Bloom filters. But if dictionary lookups are a trivial component of your overall performance, then there are no real gains to be had from replacing with a fancier data structure.

There's a follow-up point I'd like to make too. Skill with data structures is more about knowing the right questions to ask, rather than memorizing an encyclopedic list of a whole bunch of exotic structures. When you understand the major tradeoffs and design considerations underlying data structure design, then it's usually easy to find a solution in the literature even if you were completely unaware of it prior.

Effectively that means something like being able to translate between business requirements and academic language. "Oh, I need something that works on directed graphs, handles cycles, logarithmic in space, linear in average case time, resistant to adversaries, respects cache locality during lookup, and behaves deterministically."

The best way to get learn this stuff is to read a bunch of famous papers in data structure. Again, the point isn't to memorize a zoo. It's to get comfortable with the already well-developed framework that previous researchers have used. It's very unlikely that your business problem is truly unique at a theoretical level, so most likely someone before you has already figured out the tradeoffs and considerations that you are facing.

You can have twenty years experience, but if all you learn is testing frameworks, opengl, c++ libraries, etc you never really master your profession. CS fundamentals like algorithms and data structures are much more important to learn in depth. The knowledge stays with you and is always relevant, the latest testing framework, not so much.

Given the bulk of the things you've listed here, which seem to serve very practical purposes, maybe never. I wouldn't call data structures a "technology" so much as I would label them as a theoretical underpinning to much of computer science. If your goal is to learn practical technologies (which is a good goal!) you don't really _need_ to learn data structures deeply -- they're just not a skill you need in day-to-day software life (for most people). I'm a software engineer, and the idea of implementing so much as a binary heap (which is nearly one of the first data structures you learn) at work is a preposterous idea. That said, if you want to learn about data structures, go for it! They're incredibly interesting and will give you a way deeper appreciation for computer science and the work that goes into developing complex algorithms. There's just not necessarily a "pedagogical" place to stick in learning data structures with the sorts of technologies you've listed.

If I were you I'd send an email to prof. Erik Demaine about this. If you write your email thoughtfully enough, then I think he's the kind of person to answer. I would in his position send at least a quick reply.

I have the same question, except I already have a partial answer (i.e. less interested in going after it, I sometimes do these things if I'm interested enough). I'm currently working my way up 6.006, but I wonder why I'd have to do advanced data structures. My guess is: I think I don't, because it's more of a research area than anything else.

I would need to take a quick look to see if there are some interesting data structures solving some interesting problems.

Personally, I'd be interested in time travel and memory hierarchy (caching) as topics. I think they might have some immediate practical utility in those areas.

As someone in the same situation as you, I can advice that it's better to hold off on learning such things until you have a good moment to make use of them.

I studied a lot of Intel's 64 and IA-32 Architectures Software Developer Manuals, particularly the first and third volumes, and have forgotten a lot of details of what I read. I should have waited until I was ready to invest time in writing an OS or a first-stage bootloader or something. The broader things were useful, but I learned a lot of details that I haven't used and have forgotten. I'm sure it's still useful in the sense that I can quickly pick it back up if I ever needed to, but still. Same for all those things I read from the OSDev Wiki.

What do we mean by in-depth? Basic knowledge is pretty fundamental. Beyond that, I'd say assume you know them, and when you're stuck because you actually don't then go learn only as much about them to get you unstuck.