# Glossary¶

- Cost Function
- Loss Function
A mapping between an outcome and a real number that signifies the

*loss*or*cost*of that outcome. The*outcome*variable may be an event like dropping hot cup of tea, in which case the cost is some numerical value representing how terrible that is. More commonly the*outcome*variable is a vector representing, for example, the probability mass a function (like a neural network) assigned to an input.- Hessian Matrix
A square matrix of second-order partial derivatives of a function. On the diagional are the partial derivatives in a single direction, and the other spots are taken up by all the mixed-partial derivatives. Example in 2D:

\[\begin{bmatrix} \frac{\partial^2 f}{\partial x^2} & \frac{\partial^2 f}{\partial xy}\\ \frac{\partial^2 f}{\partial yx} & \frac{\partial^2}{\partial y^2} \end{bmatrix}\]- Observed Variable
A factor that is a part of a statistical relationship like a correlation or causation, and is recorded in the data at hand.

- RANSAC
Random Sample Consensus. Iterative method of fitting a model.

Draw \(s\) samples from the data.

Fit the model to these samples.

Check how many points from the full dataset fall within an acceptable range \(d\) around the model - these are inliers.

Do this for \(N\) iterations.

Choose the model with the most inliers and refit it to all inliers.

- TOML
Tom’s Obvious, Minimal Language. A readable configuration file format. Example:

title = "TOML Example" [author] name = "Stefan Wijnja" website = "https://stfwn.com" [database] server = "192.168.8.1" ports = [ 8001, 8001, 8002 ]

- Trace
The sum of the components on the main diagonal of a square matrix. The trace has the property that for three matrices \(A, B, C\): \(\mathrm{Tr}(ABC) = \mathrm{Tr}(BCA)\)

- Unit Vector
A vector with norm of \(1\), i.e.: \(\sqrt{x \cdot x} = 1\).

- Unobserved Variable
A factor that is a part of a statistical relationship like a correlation or causation, but is not recorded in the data at hand.