Two-hour ML lecture walks through MLE, ERM and EWMA from scratch
Negative_War_65 · reddit · 2026-07-28
A two-hour machine learning lecture covers the statistics foundations behind ML, including:
- maximum likelihood estimation for univariate and multivariate Gaussian models
- MLE for linear regression and residual sum of squares
- empirical risk minimization and surrogate losses
- method of moments, with its trade-offs
- exponentially weighted moving averages, including bias and its role in deep learning optimizers
The author says the material is built from scratch on a whiteboard to make the derivations and intuition easier to follow.
Related event: Two-Hour ML Statistics Course Covers MLE and ERM(2 posts)→
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