There are a few unusual parts, like the last lecture ("Large Deviations"). I'm not familiar with the entire course, but IMO the lecture on state machines is very good; it discusses invariants and uses an approchable example (the 15-puzzle).
If you have never looked at it, the problems there are very nice. For example, instead of some dry boolean logic problem about A and Not(B), you have Problem 3.17 on page 81, which begins:
This problem examines whether the following specifications are satisfiable:
1. If the file system is not locked, then. . .
(a) new messages will be queued.
(b) new messages will be sent to the messages buffer.
(c) the system is functioning normally, and conversely, if the system is
functioning normally, then the file system is not locked.
[...]
(a) Begin by translating the five specifications into propositional
formulas using the four propositional variables [...]
It's unbelievable that the average human being has access to the lectures of some of the best universities in the world for free. 31 hours of in-depth mathematics by some of the best people in their field.
Although I have always been struggling with keeping up with long lecture playlists. I always try to find shorter videos which explain the concept faster (although probably lacking depth). And end up ditching it halfway as well. Perhaps the real motivation to keep up with the material comes from actually enrolling the university? Has anyone completed such type of lectures by themselves? How do you stay consistent and disciplined?
I find courses in some platforms (coursera/khanacademy) a bit more motivating because they kind of push me with deadlines. I guess I am used to deadline-oriented studying.
Part of the value of a university is exactly that. It builds momentum and incentives. Self paces lectures can be available, but it's extremely hard to follow them if you don't have a good evaluation at the end, or if you don't have deadlines to give assignments.
But also remember, many of those lectures are at a slower peace, so one or two lectures per week. It takes time to internalize the material. People that don't follow university usually try to binge watch them, but this leads to low outcomes.
I think the best strategy is to put deadlines and risks for yourself, and follow them at a natural peace. And, do the exercices.
A lot of these topics sound interesting, though I think the average software engineer needs approximately none of that. When I first started programming, I was surprised how little mathematics was involved in practice.
Of course, these MIT lectures are aimed at computer scientists, not software engineers, which US universities consider to be quite different.
> the average software engineer needs approximately none of that.
This is not really true, especially if you're involved with physics and robotics even just a bit like a do. Without mathematics, you won't understand a thing.
I'm going to try formalizing this course in Lean--not sure how hard it is going to be. If anyone is interested in doing the same, please feel free to contribute!
Has anyone navigated a career change using OpenCourseware? I have a suspicion that the MOOC era mostly catered towards already-educated, self-starters and hobby learners, moreso than empowering a generation of workers, as was advertised.
Not to knock it. I've been working through quantum computing between work-related fire drills and household commitments, so I should be up to speed in a few decades.
Having "Mathematics for Computer Science" as a course title rubs me the wrong way, I always believed Computer Science was a specialized subfield of Mathematics.
You could make an analogous course titled "Mathematics for [subfield of mathematics]" for any subfield of math. It would be a good(ish) title (I have never titled a course), and the content would be nicely focused.
I wish these courses would also provide the answer sheets or tell you where to find them. How am I supposed to check my work and verify my answers otherwise?
20 comments
[ 3.2 ms ] story [ 47.3 ms ] threadhttps://ocw.mit.edu/courses/6-1200j-mathematics-for-computer...
https://www.youtube.com/playlist?list=PLUl4u3cNGP61VNvICqk2H...
https://ocw.mit.edu/courses/6-1200j-mathematics-for-computer...
Lecture notes:
https://ocw.mit.edu/courses/6-1200j-mathematics-for-computer...
There are a few unusual parts, like the last lecture ("Large Deviations"). I'm not familiar with the entire course, but IMO the lecture on state machines is very good; it discusses invariants and uses an approchable example (the 15-puzzle).
Text (last revised 2018): https://courses.csail.mit.edu/6.042/spring18/mcs.pdf
If you have never looked at it, the problems there are very nice. For example, instead of some dry boolean logic problem about A and Not(B), you have Problem 3.17 on page 81, which begins:
Although I have always been struggling with keeping up with long lecture playlists. I always try to find shorter videos which explain the concept faster (although probably lacking depth). And end up ditching it halfway as well. Perhaps the real motivation to keep up with the material comes from actually enrolling the university? Has anyone completed such type of lectures by themselves? How do you stay consistent and disciplined?
I find courses in some platforms (coursera/khanacademy) a bit more motivating because they kind of push me with deadlines. I guess I am used to deadline-oriented studying.
If anyone else is struggling with attention span and is looking for shorter lectures (although they may not have the same depth): https://www.youtube.com/@ProfessorDaveExplains/playlists
So many cool (free) courses and so easy to find, download.
But also remember, many of those lectures are at a slower peace, so one or two lectures per week. It takes time to internalize the material. People that don't follow university usually try to binge watch them, but this leads to low outcomes.
I think the best strategy is to put deadlines and risks for yourself, and follow them at a natural peace. And, do the exercices.
Of course, these MIT lectures are aimed at computer scientists, not software engineers, which US universities consider to be quite different.
This is not really true, especially if you're involved with physics and robotics even just a bit like a do. Without mathematics, you won't understand a thing.
I use predicates and sets quite often in daily programming.
https://github.com/dernett/Lean61200J
Not to knock it. I've been working through quantum computing between work-related fire drills and household commitments, so I should be up to speed in a few decades.
Such courses are generally titled "Intro to".