Uploaded October 2024 | Updated September 2026, 2 weeks ago
The birthday problem is fun and surprisingly challenging: How many people need to be in a room before the probability that two people share the same birthday is at least 50%? To solve this problem, we find that it is much easier to compute 1 minus the probability that two people *don't* share a birthday. This idea will be very useful in other problems.
This video was produced at the University of Washington, and we acknowledge funding support from the Boeing Company
%%% CHAPTERS %%%
00:00 Intro
02:29 Brute Force Comparisons
04:44 Brute Force Complications
06:18 The Easy Way
07:58 Why the Complement
10:01 Calculating the Complement
14:23 Homework: Computing the Complement
15:58 Warning: Factorials are Big
18:08 Outro
The birthday problem is fun and surprisingly challenging: How many people need to be in a room before the probability that two people share the same birthday is at least 50%? To solve this problem, we find that it is much easier to compute 1 minus the probability that two people *don't* share a birthday. This idea will be very useful in other problems.
This video was produced at the University of Washington, and we acknowledge funding support from the Boeing Company
%%% CHAPTERS %%%
00:00 Intro
02:29 Brute Force Comparisons
04:44 Brute Force Complications
06:18 The Easy Way
07:58 Why the Complement
10:01 Calculating the Complement
14:23 Homework: Computing the Complement
15:58 Warning: Factorials are Big
18:08 Outro
![Neural ODEs (NODEs) [Physics Informed Machine Learning]](https://i.ytimg.com/vi/nJphsM4obOk/mqdefault.jpg "Neural ODEs (NODEs) [Physics Informed Machine Learning]
This video describes Neural ODEs, a powerful machine learning approach to learn ODEs from data.
This video was produced at the University of Washington, and we acknowledge funding support from the Boeing Company
%%% CHAPTERS %%%
00:00 Intro
02:09 Background: ResNet
05:05 From ResNet to ODE
07:59 ODE Essential Insight/ Why ODE outperforms ResNet
// 09:05 ODE Essential Insight Rephrase 1
// 09:54 ODE Essential Insight Rephrase 2
11:11 ODE Performance vs ResNet Performance
12:52 ODE extension: HNNs
14:03 ODE extension: LNNs
14:45 ODE algorithm overview/ ODEs and Adjoint Calculation
22:24 Outro")
