Uploaded January 2026 | Updated September 2026, 2 weeks ago
Here we explore key properties of the MLE, such as consistency and data efficiency.
This video was produced at the University of Washington, and we acknowledge funding support from the Boeing Company
%%% CHAPTERS %%%
00:00 Intro
01:58 Property 1: MLE is Consistent
03:43 Property 2: MLE is Normal
04:41 Defining the I Function
08:04 MLE is Asymptotically Efficient
10:47 The Cramer-Rao Inequality
13:27 Outro
Here we explore key properties of the MLE, such as consistency and data efficiency.
This video was produced at the University of Washington, and we acknowledge funding support from the Boeing Company
%%% CHAPTERS %%%
00:00 Intro
01:58 Property 1: MLE is Consistent
03:43 Property 2: MLE is Normal
04:41 Defining the I Function
08:04 MLE is Asymptotically Efficient
10:47 The Cramer-Rao Inequality
13:27 Outro
![AI/ML+Physics Part 5: Employing an Optimization Algorithm [Physics Informed Machine Learning]](https://i.ytimg.com/vi/T4iJ10TAIMg/mqdefault.jpg "AI/ML+Physics Part 5: Employing an Optimization Algorithm [Physics Informed Machine Learning]
This video discusses the fifth stage of the machine learning process: (5) selecting and implementing an optimization algorithm to train the model. There are opportunities to incorporate physics into this stage of the process, such as using constrained optimization to force a model onto a susbpace or submanifold characterized by a symmetry or other physical constraint.
This video was produced at the University of Washington, and we acknowledge funding support from the Boeing Company
%%% CHAPTERS %%%
00:00 Intro
01:45 Case Study: KKT Constrained Least Squares
06:18 Case Study: Physics Informed DMD
14:00 Loss vs Optimization of Subspace Constraints
17:50 Subspace Constraints and Symmetry
19:28 Case Study: Symbolic Regression and Evolutionary Optimization
22:25 Parsimony and Sparse Optimization Algorithms
25:03 Case Study: SINDy and SR3
28:38 Parsimony and Sparsity Hyperparameters
30:55 Outro")
