In class, we learned about PAC Learning, which stands for Probably Approximately

Correct Learning. It helps us figure out the best number of hypotheses $($or Machine

Learning algorithms$),$ the right sample sizes, and the threshold that indicates when

the learning process is successful. Look at Figure $1$ as an example of convergence:

Figure $1$: Point of convergence achieved after multiple epochs. At each iteration

the error is computed. $x$ : epochs and $f(x)$ : error. You can consider it to be Mean

Square Error: ${\displaystyle \underset{i=1}{\overset{M}{}}}\frac{(y^{\text{t}\text{r}\text{u}\text{e}}-y^{\text{p}\text{r}\text{e}\text{d}\text{i}\text{c}\text{t}\text{e}\text{d}}{)}^2}{M},M$ : Number of samples that the model has

seen during training.

Given the phenomenal equation of PAC Learning:

$P[($

$u-)>lon]2|H|e^{2lo{n}^{2}M}$

where the variables are

$u,lon,|H|$ and $M.$ Describe the situation when

$P[($

$u-)>lon]$ is constant.

$|H|$ is constant

$u$ is constant

Extra points for examples in this question. $|H|$ : Number of Hypothesis

$u$ : Number of samples a model has picked during training in order to learn the task

$($can be classification or regression$)$

$P[($

$u-)>lon]$ : The likelihood of the model $($also called Probability$)$ reaching

convergence and finding a solution depends on the disparity between the machine

learning you aim to teach it $($

$u)$ and the reference model or oracle $().$