/sci/ - Science & Math

I read sciencey shit in my spare time and soak pretty much everything up like a sponge. Usually no study, but the same was true for me in class, I'm only a C/B average in maths even though i've tried.

>>2196031
how do you do that with your busy schedule of soccer and tennis?

>>2196033
Inurdaes has a groupie?

How flattering that must be.

yeah I find biology or chemistry easy to retain, but math and physics isn't really something you can cram for, its all understanding concepts and practicing applying them

>>2196033
>>2196036
Fuck I lol'd

>>2196031

Depends on subject whether I cram or not. Generally I like studying science, but I have slacked off recently.

This past week I had a chem test covering 4 chapters, didn't read anything until the night before. Read through 2, skimmed the rest and went to take the exam after the all-nighter.

End up getting highest score I've ever gotten throughout semester, EPIC WIN and A grade for the class. I no longer have to take the optional final because of the score I got, proving that procrastination works.

>>2196019
mostly, but depends on how much you need to understand.

the axioms and subspace stuff is pretty basic, no need to get more than be able to check if something is a vector/subspace.

linear independance is simple if you know some matrix algebra.

complex numbers are simple for calculating so look into them, but this s a big field so cram for what you need for the exam.

the stuff that takes time is stuff like gram schmidt (learn to not fuck it up - hand calculating this stuff always goes to shit for me even if the algorithm is pretty simple

>>2196019
I used to cruise through high school and get top marks without any study.

All changed at uni now, got my ass kicked in math, now I do proper study and brought my grades up to A's and B's.

>>2196019

For a vector space to be a subspace, the vector space must fulfill three requirements:

1. The subspace contains the 0 vector.
2. If u and v are in the vector space, then u+v is in the vector space.
3. If (cU) exists, then c(U) exists.

That's literally what you need to know. All you need to know, really.

Linear Independence: The vectors are not multiples of one another. Vector #1 is not related to Vector #2 in any way, shape or form. Linear independence is extremely important for comprehending basis/spanning sets, so if you haven't really reviewed lin. independence, I'm assuming you don't know much about the concept of spanning/basis.

Vector space axioms? I don't know what you mean by that. There's the fundamental theorem of matrices, which contains an assortment of axioms/definitions equivalent to "A is invertible" or whatever. There are a shitton of other axioms connected to linear algebra, but I'm not going to go over all of them.

>>2196255

>linear independance is simple if you know some matrix algebra.

I agree and disagree with this notion. While linear independence is simplistic alone, it is not simplistic when involved in the higher concepts(spanning, finding a basis, etc). An enormous amount of people in my linear algebra class had a very, very tough time on the last exam because they couldn't understand the concept well. When dimensions, nullity, etc. enter the picture, everything becomes confusing.

>>2196271
of course it is possible to make the concept more abstract and demanding more understanding, but the basic form of the questions on a exam are mostly related to checking to see if a set of vectors are linearly independant / if they span a V^n space,
Which is not very hard to learn or remember if one knows the algebra that is needed.

I totally agree that the more difficult aspects of it can be daunting when applying intuition, but by learning simple rules and working from a concrete example (vectors in r^3 for instance) one should be able to pass of as able on an exam for all but the cleverest of problems.

most of LA can be reduced to either doing inner products or gauss elimination on something. its just putting stuff in the right order first that needs to be crammed ;)

>>2196264
vector space axioms are the axioms that need to be satisfied by the vector space