Coding Demo: Probabilistic Machine Learning from Scratch
Negative_War_65 · reddit · 2026-08-16
This is a coding demonstration lecture based on a previous course on Probabilistic Machine Learning. The instructor writes code from scratch to clarify theoretical concepts. Topics covered include random variables and the law of large numbers, dataset visualization, EDA on the Iris dataset, classifier basics, empirical risk minimization, generalization, epistemic and aleatoric uncertainties, Softmax and LogSumExp tricks, linear models, maximum likelihood estimation, and building an end-to-end ML pipeline.
More from Research
- NEO原生多模态架构：从零训练挑战视觉编码器 | 论文发布 — liuziwei7 · 2026-08-16
- Predictive Rosenblatt Spline offers closed-form inverse for normalizing flows — Algomancer · 2026-08-16
- Harvard's COMPASS model predicts immunotherapy response from tumors — zakkohane · 2026-08-16
- Deep RL Books Criticized for Spending 50-70% on Tabular Methods — burkov · 2026-08-16
- 4D-WAM: Training world action models with 3D trajectory supervision, zero inference overhead — 量子位 · 2026-08-16
- 5B LittleLearner trained only on K-5 material to test LLM limits — MatthewMcAteer0 · 2026-08-16