A two-hour AI/ML stats lecture covers MLE, ERM, and exponentially weighted averages
Negative_War_65 · reddit · 2026-07-28
A two-hour lecture on statistics for AI/ML covers maximum likelihood estimation for univariate and multivariate Gaussians, linear regression, residual sum of squares, empirical risk minimization, surrogate losses, method of moments, and exponentially weighted moving averages.
The author says the course is built from scratch on a whiteboard to make the derivations and intuition easier to follow, and shared it as part of a broader machine-learning lecture series.
Related event: Two-Hour ML Statistics Course Covers MLE and ERM(2 posts)→
More from Research
- A catalog of failed AI predictions is being used to push back on hype — StanfordAILab · 2026-07-28
- Codabench launches a public pan-cancer CT segmentation challenge — maier_ak · 2026-07-28
- David Baker’s open-science lab culture is reshaping AI-era protein design — xiaofei_lin · 2026-07-28
- FilmBench evaluates cinematic video generation with film-school shot lists and 35 metrics — Shengyi Wang · 2026-07-28
- Frozen 12B system reuses verified memory at zero tokens and 6,000,000-token context — Corbenci · 2026-07-28
- VLM evidence attribution improves with quote-and-retrieve, not bounding boxes — Zhuchenyang Liu · 2026-07-28