Ok, I almost forgot this blog existed, but we are back. It's been really a hectic year honestly. Let's get into it. What is Probabilistic Machine Learning ? What is Machine Learning to start with ? At its core, machine learning is about finding patterns in data. Let's say given some data $X$ , we want to either find structure within it, or learn a mapping from it to some output $Y$. here, we are basically looking for a function $f$ such that $y = f(x) , x \in X , y \in Y$ In practice, $f$ is usually parameterized by some set of weights $W$ , whether it's a linear model, a decision tree, or a neural network, the idea remains same. We tune $W$ until $f$ maps the inputs to the outputs, well enough, which is essentially an optimization problem. So far so good but the problem is real world is messy and often not so deterministic, and the data we collect is not the whole story. The data $X$ and/ or $Y$ itself is a finite sample drawn from some much larger underly...