google NAVEDTRA books.

Any textbook will really work. Khan Academy is a good online source for getting ahead. Best of luck

You will want Calculus, Linear Algebra, Differential Equations, Partial Differential Equations, Discreet Mathmatics, Complex Variables, Proofs, and Abstract Algerbra. There may be others but I can't think of them right now.

Fucking, yes, so boringggggggg.

My teacher is probably the best and most well liked in the school, so I love them.

>microscopic telescope
What do you mean? A telescope of microscopic proportions, or a telescope able to detect micro-scale features of objects at astronomical distances? Judging by the rest of your post, I'm going to assume the latter.

>produce a 3d environment of that area through the lens
What? What microscope or telescope is able to do this? All a scope does is take a cone of light and focus it to make it appear larger, and thus easier to see to human beings (obviously this doesn't apply to electron microscopes which work by entirely different principles).

>Surely theres an infinite amount of detail from a physical item
No. Photons are discrete particles. It's possible for an object to be so far away that even if we convert the entire surface of the Earth into a receiving device, not enough photons from the object will hit the Earth during its entire lifetime to get even sub-meter resolution. To get sub-meter resolutions within a human lifespan for relatively close objects (say, 1 light year) would require astronomically large objective lenses. Or, the detection device would have to move really fast around the target, which would increase the resolution only in the dimension of motion.
For example, the LIGO experiment, which is believed to have detected gravity waves from the black hole at the center of the galaxy, uses "lenses" (they're not actual lenses, but they're analogous) with a radius of 4 km.

contect: https://youtu.be/7XTe9lDiAhw

>>16281
Yes...?

What you'd want to do instead is create an agent that understands the allowed moves. Use multiple agents to play against each other. Incentivize the agents by assigning weights to different conditions. Things like -1 point for losing a piece, +1 for taking another player's piece, -100 for losing the game, +100 for winning. Then let the agents play each other a couple hundred thousand times and store the patterns.

The system would then follow paths in a decision tree for each state of the game to the next state, following the higher point paths.

What you've described (The recording and analyzing would be a very minor part, and observing enough games to program an AI would be difficult if they were real games.)

Other options include incentivizing other goals, like control of the middle of the board, or attacking the player with the most pieces. This could lead to other strategies that the system could then utilize.

Look up reinforcement learning. But for Chinese checkers ab pruning should suffice

I'm basically at the same tier you are or even dumber, but one thing I know is in neutrinos. The SM predicts that they are massless, but from observations they have to have mass. Because they oscillate, they need to have different mass eigenstates to allow for different wavelengths to interfere or something.

>>16314
>Physics
>Youtube - Toggle Video
>does anyone think that some of the theories of the Standard Model (e.g. Heisenberg Uncertainty Principle, Fermat's Principle) could be ~debunked~ or proven to be false?
Not any time soon.

>How close is what we know now to fully understanding all of physics?
Not even close.

I will say this: Debunking the standard model is unlikely, however it is without doubt incomplete. It is a very interesting area to look into and there is plenty of current reasearch in this direction. You could go for a theoretical side of things which would be quantum field theory or a experimental side which looks at incompatible data with the model. Indeed the standard model is considered the most complete and accurate success of physics, however it is by no means close to enough.