Law of cosines
Episode 5: Sines And Cosines Part II - Project MATHEMATICS!
Polynomial functions
def parabola(x, h, k):
return a * math.pow(x - h, 2) + k
def cubicpolynomial(x, a, b, c):
return math.pow(x, 3) + (a * math.pow(x, 2)) + (b * x) + c
Optimization
Optimization is at the core of machine learning. It's what's used to "train" neural networks.
Objective functions
$$ \min \sum_{t=1}^{d} \sum_{i=1}^{n} \| \vec{P}_i - \vec{V}_i \|^2 $$Local and global optima
Coordinate descent
Genetic algorithms
Minima and maxima
Multiple objective optimization
Many problems in optimization are multii objective problems where you'd be looking for both maxima and minima.
Curve fitting
Genetic programming
Linear least squares
$$ g(x,y) = \frac{1}{2\pi\sigma^2}e^{-\frac{x^2+y^2}{2\sigma^2}}, $$
Formula for any Gaussian distribution