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"Mathematical ideas are by nature precise and well defined, so that a precise description is possible in a very short space" "A well-written math text will be

"Mathematical ideas are by nature precise and well defined, so that a precise description is possible in a very short space" "A well-written math text will be careful to use a word in one sense only" So many papers are not that careful, and thus, not so precise. There are only so many greek letters, so they end up overloaded. I can't count the number of times I've struggled over an equation, only to find out that the reason it made no sense and/or the value for a specific example came out differently was because the author assumed one variable to mean something differently than I did (and my assumption was perfectly valid in another context. Sometimes another context in -the same paper-). The whole experience is incredibly frustrating. Echoing another point here, if you just show a sufficiently non-trivial example and walk me through it, I grok it quickly. -Then- give me the equation if you must.

vidarh 9y ago
My main exposure to mathematics is via CS papers, and this is doubly true there it seems to the point where my warning bells go off if they present their solutions in equations - it's all to often a sign that they're going to hand-wave away essential details that'd be obvious were missing if they presented code or even pseudo-code. E.g. I did my MSc. on statistical approaches to reducing error rates in OCR, and so many of the papers I reviewed for my literature review suffered from leaving out absolutely critical information when they presented equations etc. that most of the time I spent implementing a lot of the methods presented tended to be to try to reverse engineering missing information (and often that was only possible because many of the papers used one of a few well established public datasets for their experiments). To me, for those CS papers, maths was a warning what was lying ahead was likely not precise or well defined. It's incredibly frustrating as there were generally no good reason to do so - it wasn't a matter of leaving out large bulks of complicated code. Often it could be as simply as leaving out the concrete values of given parameters. I'm not implying maths have no place in CS papers, but the CS papers that I've seen that have used it best have tended to also present code fragments, or use maths very sparingly and coupled with painstakingly defining variables etc.
- lostcolony 9y ago
  Yeah, my attempt at a master's in CS (before deciding the time would be better spent elsewhere) left me feeling largely the same. "Oh, isn't that equation elegant. Oh, wait, actually trying to implement it...what the hell, where is all the other information I need?! That's a gaussian...but where do the parameters for that gaussian come from? What is this variable? WHAT IS THIS VARIABLE?!" etc. I had a 4.0 departmental in CS from the same school I tried to get a master's in, but yeah, the master's experience was so bad, and working through those issues so time consuming, I just said "Screw it, I can get a better return on this amount of time doing my own things". Even if it's learning the same things, but to an explicit goal rather than just 'understanding' (and an eventual test/assignment that may or may not relate to what I care about), and where I'm not constrained by an academic policy that prevents me from going to others to have them explain exactly what I need to know.
neaden 9y ago
Another problem is that different subfields of math use jargon a little bit differently. Normal for instance can mean so many different things.

vidarh 9y ago

My main exposure to mathematics is via CS papers, and this is doubly true there it seems to the point where my warning bells go off if they present their solutions in equations - it's all to often a sign that they're going to hand-wave away essential details that'd be obvious were missing if they presented code or even pseudo-code. E.g. I did my MSc. on statistical approaches to reducing error rates in OCR, and so many of the papers I reviewed for my literature review suffered from leaving out absolutely critical information when they presented equations etc. that most of the time I spent implementing a lot of the methods presented tended to be to try to reverse engineering missing information (and often that was only possible because many of the papers used one of a few well established public datasets for their experiments). To me, for those CS papers, maths was a warning what was lying ahead was likely not precise or well defined. It's incredibly frustrating as there were generally no good reason to do so - it wasn't a matter of leaving out large bulks of complicated code. Often it could be as simply as leaving out the concrete values of given parameters. I'm not implying maths have no place in CS papers, but the CS papers that I've seen that have used it best have tended to also present code fragments, or use maths very sparingly and coupled with painstakingly defining variables etc.

lostcolony 9y ago
Yeah, my attempt at a master's in CS (before deciding the time would be better spent elsewhere) left me feeling largely the same. "Oh, isn't that equation elegant. Oh, wait, actually trying to implement it...what the hell, where is all the other information I need?! That's a gaussian...but where do the parameters for that gaussian come from? What is this variable? WHAT IS THIS VARIABLE?!" etc. I had a 4.0 departmental in CS from the same school I tried to get a master's in, but yeah, the master's experience was so bad, and working through those issues so time consuming, I just said "Screw it, I can get a better return on this amount of time doing my own things". Even if it's learning the same things, but to an explicit goal rather than just 'understanding' (and an eventual test/assignment that may or may not relate to what I care about), and where I'm not constrained by an academic policy that prevents me from going to others to have them explain exactly what I need to know.

neaden 9y ago

Another problem is that different subfields of math use jargon a little bit differently. Normal for instance can mean so many different things.