Notes on AI, ML, Software Engineering and Math
In the previous post, we discussed how to model player skills using normal distributions. Now, let’s dive into the mathematical details of how we can estimate the parameters of these distributions using Maximum Likelihood Estimation.
Problem definition: Having a set of players \(\mathcal{P}\) numbered from \(1\) to \(n …
The idea struck me during a friendly table tennis tournament. We had a marathon of matches to rank ourselves by skill, and somewhere in the middle of all those games, a question popped into my mind: How can we estimate players’ skill levels \(\mathcal{S}\) using just the game outcomes …