This is just a joke, so do not follow it seriously…

The very usual case in modern machine learning is as follows:

- design a model to describe the data, for example, suppose some kind of 2D points are generated along a quadratic curve y = a*x^2 + b*x + c, and
- design an algorithm that estimates the model parameters, in our case, a, b, and c, given a set of data (observations), x_1,y_1,x_2,y_2,…x_n,y_n.
- The model parameters can be used in some way, say, given a new x and predict its corresponding y.

So, when I was reading Prof. Feynman’s lecture notes, which mentions Cavendish experiment, I thought this experiment is some kind of “learning using machines” — using the specially designed equipment (machine), Cavendish measured the gravitational constant G in Newton’s law of universal gravitation:

F = G * m1 * m2 / r^2

And, using the estimated model parameter G, we can do somethings interesting. For example, measure the weight of the earth (by measuring the weight/gravity F of a known small ball m1, and put them back into the equation to get m2, the mass of earth).

However, this is a joke as I said so you cannot use it in your lecture notes on machine learning. The fact was that Cavendish did not measure G as stated in many textbooks. Instead, he measures the earth directly by comparing (1) the force that a big ball with known mass attracts a small ball with (2) the force that the earth attracts the small ball. If the ratio (2)/(1) is N, then the earth is N times weight of the big ball.

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