Uploaded February 2026 | Updated September 2026, 2 weeks ago
This video introduces Bayesian inference and statistics, which is a powerful framework for learning distributions from data.
This video was produced at the University of Washington, and we acknowledge funding support from the Boeing Company
%%% CHAPTERS %%%
00:00 Intro
01:09 Big Idea: Bayesian Inference
06:04 BI Balances Data and Prior Beliefs
09:43 BI Yields Distribution for Theta
11:01 The Bayesian Update
14:29 Code Demo: Bayesian Hypothesis Testing
23:21 Disadvantages of BI
27:53 Outro
This video introduces Bayesian inference and statistics, which is a powerful framework for learning distributions from data.
This video was produced at the University of Washington, and we acknowledge funding support from the Boeing Company
%%% CHAPTERS %%%
00:00 Intro
01:09 Big Idea: Bayesian Inference
06:04 BI Balances Data and Prior Beliefs
09:43 BI Yields Distribution for Theta
11:01 The Bayesian Update
14:29 Code Demo: Bayesian Hypothesis Testing
23:21 Disadvantages of BI
27:53 Outro
![AI/ML+Physics: Preview of Upcoming Modules and Bootcamps [Physics Informed Machine Learning]](https://i.ytimg.com/vi/_ObvDgPMWkU/mqdefault.jpg "AI/ML+Physics: Preview of Upcoming Modules and Bootcamps [Physics Informed Machine Learning]
This video provides a brief preview of the upcoming modules and bootcamps in this series on Physics Informed Machine Learning. Topics include: (1) Parsimonious modeling and SINDy; (2) Physics informed neural networks (PINNs); (3) Operator methods, like DeepONets and Fourier Neural Operators; (4) Symmetries in physics and machine learning; (5) Digital Twin technology; and (6) Case studies in engineering.
This video was produced at the University of Washington, and we acknowledge funding support from the Boeing Company
%%% CHAPTERS %%%
00:00 Intro & Recap
01:06 Reviewing the 5 Stages
04:08 Reviewing Physics in the Stages
05:11 Why Physical Models: Cost & Data Scale
07:53 Why Physical Models: Generalized Models
10:01 Why Physcial Models: Discovering Physics
11:40 Holistic Impact of Embedding Physics // Struggling to find a good wording here
12:55 Case Study: Pendulum Data and SINDy
15:20 Case Study: Symbolic Regression and Evolutionary Optimization
16:45 Case Study: Lagrangian Neural Networks
18:34 Architectures and Symmetries
19:36 Applications in Engineering
21:21 The Digital Twin
22:15 Benchmark Problems
23:35 Outro")
