Mathematics for Computer Science - Lecture Notes | Hacker News Reader

Mathematics for Computer Science - Lecture Notes

ocw.mit.edu

176 pointsiamanet26 comments

This is useful, but pretty standard undergrad material. There are more interesting OCW courses, such as Street Fighting Mathematics:

This course teaches the art of guessing results and solving problems without doing a proof or an exact calculation. Techniques include extreme-cases reasoning, dimensional analysis, successive approximation, discretization, generalization, and pictorial analysis. Applications include mental calculation, solid geometry, musical intervals, logarithms, integration, infinite series, solitaire, and differential equations. (No epsilons or deltas are harmed by taking this course.)

http://ocw.mit.edu/courses/mathematics/18-098-street-fightin...

cool, are there any vids for this course?

Nope there are no videos but the notes are simple to understand since they are very clearly explained.

"singing logarithms" - just like Anathem!

Note that the course's textbook, which is also called Street Fighting Mathematics, is available via Creative Commons Noncommercial Share Alike:

http://mitpress.mit.edu/catalog/item/default.asp?ttype=2&...

Its a link on the left side under the picture.

Simple put: awesome

this reminds me of when I took partial differential equations where the instructor said: "this shit is really hard, so we are going to learn to guess the solution so we can even have a chance at getting close to the solution".

I have always been intimidated by college level math and secretly wished I was smarter to not just understand, but to enjoy it as so many people seemingly do. I tried but failed to attain that level of proficiency multiple times.

The mistake I was making was that I was trying to read lectures/blogs/books recommended by random people who knew nothing about me. It doesn't work. The thing is: math is big. It's HUGE, it's a whole world with something for everyone, and not everything in there excites me. I discovered this by accident by picking an easy and fun book about cryptography called (I think it was called The Code) - it was fascinating, it took me to wikipedia and I started to explore this world following my own interests And then it clicked: suddenly even "boring" aspects of math showed me their exciting sides.

Yeah, somewhat a really good teacher helps students to like what they need to learn.. that make a huge difference. When you want to learn it because you like that, everything become easy.

Is the book you were referring to "The Code Book: The Science of Secrecy from Ancient Egypt to Quantum Cryptography" by Simon Singh?

Amazon link - http://amzn.to/dlNAeO

It's on my reading shelf right now, but I have not gotten around to reading it. I think your comment just might make that happen. Thanks for the recommendation

I've read that book although I admit I did not take the final challenges. Still I found that to be a very interesting book, very well written where you can actually learn something useful.

What you say is very true. I guess the whole idea of this course was to equip students with the necessary Math skills to comprehend courses like Algorithms and data structures especially the CLRS's 'Introduction to Algorithms' book. At the least these notes helped me comprehend that book ;-)

This is stuff that's usually taught in a course called "Discrete Mathematics". As far as textbooks go for this type of material, which do you all like? I own Rosen's famous book ( https://www.amazon.com/Discrete-Mathematics-Applications-Ken... ), but it's the "James Stewart's Calculus" of Discrete Mathematics books. I purchased some really old books too, but I haven't yet found one that I like a lot (I settled for a Dover text that's mostly passable).

For the specific topic of set theory, though, I haven't found one I like better than Paul Halmos: https://www.amazon.com/Naive-Theory-Undergraduate-Texts-Math...

If I could only find a number theory text that I like as much.

Good find. Note that the Fall2005 offering includes solutions to the assignments and in-class problems while Spring2005, the one linked, does not.

http://ocw.mit.edu/courses/electrical-engineering-and-comput...

Do the page "The ZFC Axioms" contain errors? I can't understand the following proposition:

∃y∀z(∃w(z ∈ w ∧ w ∈ x) ⇒ z ∈ y)

Shouldn't it be:

∃y∀z(∃w(z ∈ w ∧ w ∈ y) ⇒ z ∈ y)

are there any lecture videos available for this course?

Nope ;-) I even searched for related math books by these course instructors but no luck.

Mathematics gets way more attention than it deserves in computing -- particularly with today's sophisticated libraries/frameworks.

Just because a topic can be challenging doesn't automatically make it worthwhile.

This is a trap that many people fall into.

I think the more common trap, into which you appear to have fallen, is thinking that computer science and software engineering are the same thing.

The other common trap to fall into: thinking that all programming problems are solved. All you have to do is code the "business logic".

A corollary of this is that it's impossible to speed up "business logic" at all. You have to drop from Java (typically) directly into assembly language.

Not all programming problems have been solved, but your time is not unlimited either. You need to spend your time on what really matters for your development.

Who are you to say that this doesn't matter for someone's development? This thread reads like you're trying to convince yourself that you don't need to understand this stuff.

I've taken quite a lot of university math courses. Not much is helpful for what I do in my development.

In fact, not even the algorithms courses I've taken are all that helpful since most of the hard stuff is already done for you in libraries/frameworks.

to what I do in my development.

What I pointed out above is that there are people who do this stuff in their development. (-:

It seems like we're caught in this cycle where every 2 or 3 years, a new framework comes out that everybody rushes to embrace. Someone is writing the code to build these frameworks, and I guarantee you someone had to worry about what made their algorithm O(n^2) instead of O(log(n))

I think the reason you're being downvoted though, isn't because you simply pointed out that some people don't need to know this stuff, because (although somewhat pointless to state), that's true. Rather, I think the problem was when you said

Mathematics gets way more attention than it deserves in computing

Because you made no attempt to back it up, producing only evidence of your particular situation.

It seems like we're caught in this cycle where every 2 or 3 years, a new framework comes out that everybody rushes to embrace. Someone is writing the code to build these frameworks, and I guarantee you someone had to worry about what made their algorithm O(n^2) instead of O(log(n))

The number of people working on difficult libraries/frameworks is rather small compared to the total number of developers.

Many of those people read this site, however.

Do you not do anything algorithmic in your line of work?

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