Discussion in 'General Discussion' started by Arnie, 26 Apr 2012.
probably maths based electrical laws, thats some crazy **** once you get into it!
Exactly where I got too.
Never found a sensible use for it though
My Stats 1 module though has been a gold mine of reports, uni work and disproving lecturers for their mad (read TERRIBLE) use of statistics on nationalism.
I learnt this too, and also that 0.9r = 1
I did the Differential Equations, but did Matrices last year which I think is technically FP1/2 level.
My general 'stats' knowledge vastly outweighs my maths knowledge though. DW tests, F-Tests, T-Tests, Gauss Markov Theorem's etc... A lot of Econometrics basically.
Maths is entirely overrated......it has no real-world application whatsover and we would be all better off learning something constructive, something like pancake tossing.
I was going to leave the 's' off Maths, but didn't want to overdo it......
Fixed for you.
I remember doing something at university to do with Gauss and magnetic flux, but on top of that stuff like Laplace and Fourier transforms I could never get my head round. Long forgotten though.
FFT's and gaussian elimination were a bi**h, in C, even worse.
Ha! Century old texts are for beginners.....I have millennia old ones to play with now....
Quite right too, Pancake tossing is a much needed skill......
Not really super complicated but it's what I use at work.
Regression and correlation analysis and gauge R&R (repeatability and reproducibility)
which are Six Sigma tools.
Grobner bases are probably the most complicated thing I've studied at uni, thankfully we have programs like Maple to help out...
Any part of my degree lol.
For me, control engineering with transforms, laplace, fourier etc.
It's just taught to us by someone with poor English, in parrot form.
1+1 = 2
At uni I studied a lot of Artificial Intelligence stuff, I understood the concepts quite easily in comparison to the maths that they go into. E.g. For bio-inspired computing would cover how neural networks can be used to learn patterns, and how 'energy' stabilises. Computer vision was probably the most intensive, with one of the hardest stuff to grasp was how to recognise 'depth' in an image using all sorts of algorithms.
Supposedly, set theory, Fourier analysis, and so on, in reality, not much
Fractions, other than halves and quarters.
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