# Life, the Universe and Machine Learning

St George’s Church Lecture Theatre, University of Sheffield

## $y= mx+ c$

point 1: $x= 1$, $y=3$ $3 = m + c$

point 2: $x= 3$, $y=1$ $1 = 3m + c$

point 3: $x= 2$, $y=2.5$

$2.5 = 2m + c$

## $y= mx+ c + \epsilon$

point 1: $x= 1$, $y=3$ $3 = m + c + \epsilon_1$

point 2: $x= 3$, $y=1$ $1 = 3m + c + \epsilon_2$

point 3: $x= 2$, $y=2.5$ $2.5 = 2m + c + \epsilon_3$

## A Probabilistic Process

Set the mean of Gaussian to be a function. $p\left(y_i|x_i\right)=\frac{1}{\sqrt{2\pi\sigma^2}}\exp \left(-\frac{\left(y_i-f\left(x_i\right)\right)^{2}}{2\sigma^2}\right).$

This gives us a ‘noisy function’.

This is known as a stochastic process.

## Hydrodynamica

Entropy:
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## Data Fit Term

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ODE1 ARTIFICIAL EXAMPLE

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Eddington, A.S., 1929. The nature of the physical world. Dent (London). https://doi.org/10.2307/2180099

Goodsell, D.S., 1999. The molecular perspective: P53 tumor suppressor. The Oncologist, Vol. 4, No. 2, 138-139, April 1999 4, 138–139.

Mikhailov, G.K., n.d. Daniel bernoulli, hydrodynamica (1738), in:.

Piazzi, G., n.d. Fortgesetzte nachrichten über den längst vermutheten neuen haupt-planeten unseres sonnen-systems, in:. pp. 279–283.