Justement, oui ; j'ai vécu une belle histoire avec les mathématiques...
Quand donc les choses se sont-elles gâtées ? Quelque part entre la rentrée en prépa scientifique et ... la première semaine de prépa scientique, il me semble.
Néanmoins, l'eau a largement coulé sous les ponts depuis les derniers outrages subis autant qu'infligés, et le temps est venu de renouer cette idylle.
J'ai donc pu méditer ce conseil de Steve Yegge.
The right way to learn math is breadth-first, not depth-first. You need to survey the space, learn the names of things, figure out what's what.
Why? Because the first step to applying mathematics is problem identification. If you have a problem to solve, and you have no idea where to start, it could take you a long time to figure it out. But if you know it's a differentiation problem, or a convex optimization problem, or a boolean logic problem, then you at least know where to start looking for the solution.
[...] So it probably wasn't a great idea to make you spend years and years doing proofs and exercises with them, was it? If you're going to spend that much time studying math, it ought to be on topics that will remain.
[...] Which is why I think they're teaching math wrong. They're doing it wrong in several ways. They're focusing on specializations that aren't proving empirically to be useful to most high-school graduates, and they're teaching those specializations backwards. You should learn how to count, and how to program, before you learn how to take derivatives and perform integration.
REM : notons tout de même ici un parti-pris pour les maths discrètes vs maths continues ; quelques scientifiques pourront être en désaccord, selon leur domaine d'activité.
Une dernière citation de l'article, qui cite des motivations pratiques pour (ré-)apprendre les maths (se muscler le cerveau ; se doter d'outils pour coder dans des domaines passionants) :
For me, I've noticed that a few domains I've always been interested in (including artificial intelligence, machine learning, natural language processing, and pattern recognition) use a lot of math. And as I've dug in more deeply, I've found that the math they use is no more difficult than the sum total of the math I learned in high school; it's just different math, for the most part. It's not harder.
A côté de ces motivations pratiques, j' aimerais également acquérir une vision globale et historique ; vision qui est complétement absente des manuels scolaires.
Voici donc ma liste de lecture :
Abrégé d'histoire des mathématiques
Arnaud-Aaron Upinsky : les mathématiques
Concernant l'informatique :
Méthodes mathématiques pour l'informatique
Auquel il conviendra d'adjoindre Théorie des graphes
Cité dans l'article : Richard Gabriel - Patterns Of Software
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