Uploaded July 2025 | Updated September 2026, 2 weeks ago
Here we introduce one of the most important results in probability and statistics: the central limit theorem. The theorem states that under mild assumptions, the sum of i.i.d. random variables tends to converge to a normal distribution. This is useful in a number of scenarios including in survey sampling in statistics.
This video was produced at the University of Washington, and we acknowledge funding support from the Boeing Company
%%% CHAPTERS %%%
00:00 Intro
02:13 Statement of the CLT (Sample Mean)
05:35 Proof of CLT (Sketch)
07:40 Statement of the CLT (Sum of Variables)
09:18 Outro
Here we introduce one of the most important results in probability and statistics: the central limit theorem. The theorem states that under mild assumptions, the sum of i.i.d. random variables tends to converge to a normal distribution. This is useful in a number of scenarios including in survey sampling in statistics.
This video was produced at the University of Washington, and we acknowledge funding support from the Boeing Company
%%% CHAPTERS %%%
00:00 Intro
02:13 Statement of the CLT (Sample Mean)
05:35 Proof of CLT (Sketch)
07:40 Statement of the CLT (Sum of Variables)
09:18 Outro
![Python Symbolic Regression (PySR) [Physics Informed Machine Learning]](https://i.ytimg.com/vi/df43V4OjMVs/mqdefault.jpg "Python Symbolic Regression (PySR) [Physics Informed Machine Learning]
This video was produced at the University of Washington, and we acknowledge funding support from the Boeing Company
%%% CHAPTERS %%%
00:00 Intro
02:14 What is Symbolic Regression?
04:08 High-Level Algorithm/ Genetic Programming Overview
// 05:26 Crossover and Mutation
08:12 History of Symbolic Discovery/ Background: Symbolic Discovery
// 09:16 Symbolic Discovery Case Study
// 11:15 Case Study: Control Laws
12:18 Why PySR
// 12:18 PySR: Features
// 13:15 PySR: Benchmarks
// 14:00 PySR: Code Structure
14:19 Why Symbolic Regression/ Potential Outro
15:48 Outro")
