Maximum Likelihood Estimation (MLE) is basically the engine under the hood of most ML models, but it's often glossed over in beginner tutorials. If you don't actually grasp why we use MLE or how it connects to KL divergence, model fitting just feels like magic rather than math.
If you're struggling with the theory side of things, this breakdown is a solid starting point for a real-world understanding of the stats involved.
I've been digging into the relationship between these concepts to get a better handle on parameter estimation across different distributions. It's one thing to run a library call, but actually justifying the use of MLE through the lens of information theory makes the whole AI workflow click.
For anyone trying to build a foundation from scratch, focusing on these three areas is key:
Model Fitting: Understanding the actual objective function.MLE Justification: Why we maximize likelihood instead of just "guessing" parameters.KL Divergence: The theoretical bridge that explains how we minimize the difference between predicted and actual distributions.
If you're struggling with the theory side of things, this breakdown is a solid starting point for a real-world understanding of the stats involved.
https://youtu.be/MwTeQVVYtOc?si=UxNOGtqopzJppXAT
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Using log-likelihood usually makes the math way easier when dealing with products of probabilities.
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