
Ratio Control and Scaled Signal Calculations
When and how to use ratio, and how to implement within standard scaled signals
See MoreData-Driven Control: Balancing Example
In this lecture, we give an example of how a change of coordinates can balance the controllability and observability of an input—output system.
See MoreDynamic Mode Decomposition (Code)
In this video, we code up the dynamic mode decomposition (DMD) in Matlab and use it to analyze the fluid flow past a circular cylinder at low Reynolds number.
See MoreSingular Value Decomposition (SVD): Matrix Approximation
This video describes how the singular value decomposition (SVD) can be used for matrix approximation.
See MorePrincipal Component Analysis (PCA) [Matlab]
This video describes how the singular value decomposition (SVD) can be used for principal component analysis (PCA) in Matlab.
See MoreStanford CS234: Reinforcement Learning | Winter 2019 | Lecture 4 - Model Fre...
Professor Emma Brunskill
Assistant Professor, Computer Science
Stanford AI for Human Impact Lab
Stanford Artificial Intelligence Lab
Statistical Machine Learning Group
RL Course by David Silver - Lecture 10: Classic Games
An overview of Game Theory, minimax search, self-play and imperfect information games.
See MoreBuilding a Matlab/Simulink Model of an Aircraft: the Research Civil Aircraf...
In this video we implement the RCAM model as a Matlab script that is called from a Simulink model. The result is a fully encapsulated Simulink model of a nonlinear, 6 DOF aircraft. Please...
See MoreData-Driven Control: Eigensystem Realization Algorithm
In this lecture, we introduce the eigensystem realization algorithm (ERA), which is a purely data-driven algorithm to obtain balanced input—output models from impulse response data. ERA was...
See MoreParseval's Theorem
Parseval's theorem is an important result in Fourier analysis that can be used to put guarantees on the accuracy of signal approximation in the Fourier domain.
See MoreSVD: Eigenfaces 1 [Python]
This video describes how the singular value decomposition (SVD) can be used to efficiently represent human faces, in the so-called "eigenfaces" (Python code, part 1).
See MoreIdentifying Dominant Balance Physics from Data - Jared Callaham
This video illustrates a new algorithm to identify local dominant physical balance relations from multiscale spatiotemporal data.
See MoreStanford CS234: Reinforcement Learning | Winter 2019 | Lecture 16 - Monte Ca...
Professor Emma Brunskill
Assistant Professor, Computer Science
Stanford AI for Human Impact Lab
Stanford Artificial Intelligence Lab
Statistical Machine Learning Group
Using a Homogeneous Transformation Matrix to Combine Rotation and Translatio...
In this video we discuss how to properly deal with coordinate frames that are both rotated and translated from one another. We develop a homogeneous transformation matrix which combines a...
See MoreVirtual Laboratory for Vibrations and Control Theory
This virtual lab developed in Matlab Simscape provides an innovative tool for learning and teaching fundamentals of mechanical vibrations including mass-spring-damper systems.
See MoreData-Driven Control: Observer Kalman Filter Identification
In this lecture, we introduce the observer Kalman filter identification (OKID) algorithm. OKID takes natural input--output data from a system and estimates the impulse response, for later...
See MoreKoopman Spectral Analysis (Representations)
In this video, we explore how to obtain finite-dimensional representations of the Koopman operator from data, using regression.
See MoreSVD: Eigenfaces 4 [Matlab]
This video describes how the singular value decomposition (SVD) can be used to efficiently represent human faces, in the so-called "eigenfaces" (Matlab code, part 4).
See MoreUnitary Transformations and the SVD [Python]
This video describes how the singular value decomposition (SVD) is related to unitary transformations, with Python code.
See MoreStanford CS234: Reinforcement Learning | Winter 2019 | Lecture 3 - Model-Fr...
Professor Emma Brunskill
Assistant Professor, Computer Science
Stanford AI for Human Impact Lab
Stanford Artificial Intelligence Lab
Statistical Machine Learning Group
RL Course by David Silver - Lecture 4: Model-Free Prediction
An introduction to Monte-Carlo Learning and Temporal Difference Learning
See MoreSimple Vector Mechanics: Inner Product, Scalar/Vector Projection, and Cross ...
In this video we discuss several simple vector operations such as: 1. Computing the magnitude of a vector 2. The inner/dot product 3. The scalar and vector projection 4. The cross product
See MoreCoriolis Effect Demonstration (with Drones)
We demonstrate how rotating reference frames give rise to the Coriolis effect and centrifugal acceleration. In this video, we approach this as a simple physics demonstration and examine...
See MoreData-Driven Control: Balanced Truncation Example
In this lecture, we explore the balanced truncation procedure on an example in Matlab. In particular, we demonstrate the ability of a balancing transformation to make the controllability...
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