Data-Driven Control: Balancing Transformation

In this lecture, we derive the balancing coordinate transformation that makes the controllability and observability Gramians equal and diagonal. This is the critical step in balanced model...

Dynamic Mode Decomposition (Examples)

In this video, we continue to explore the dynamic mode decomposition (DMD). In particular, we look at recent methodological extensions and application areas in fluid dynamics, disease...

SVD and Alignment: A Cautionary Tale

This video describes the importance of data alignment when performing the singular value decomposition (SVD). Translations and rotations both present challenges for the SVD.

Linear Regression 1 [Python]

This video describes how the singular value decomposition (SVD) can be used for linear regression in Python (part 1).

Stanford CS234: Reinforcement Learning | Winter 2019 | Lecture 2 - Given a M...

Professor Emma Brunskill
Assistant Professor, Computer Science
Stanford AI for Human Impact Lab
Stanford Artificial Intelligence Lab
Statistical Machine Learning Group

Data-Driven Control: Eigensystem Realization Algorithm Procedure

In this lecture, we describe the eigensystem realization algorithm (ERA) in detail, including step-by-step algorithmic instructions.

Velocity & Acceleration in Non-Inertial Reference Frames (Coriolis &...

In this video we derive a mathematical description of velocity and acceleration in non-inertial reference frame. We examine the effect of fictitious forces that are witnessed by observers on...

SVD: Eigenfaces 2 [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 2).

MATLAB Sensor Array Analyzer App

The Sensor Array Analyzer app enables you to construct and analyze common sensor array configurations. These configurations range from 1-D to 3-D arrays of antennas, sonar transducers, and...

Stanford CS234: Reinforcement Learning | Winter 2019 | Lecture 12 - Fast Rei...

Professor Emma Brunskill
Assistant Professor, Computer Science
Stanford AI for Human Impact Lab
Stanford Artificial Intelligence Lab
Statistical Machine Learning Group

Teaching resources for a reinforcement learning course

Teaching resources by Dimitri P. Bertsekas for reinforcement learning courses. The website has links for freely available textbooks (for instructional purposes), videolectures, and course...

Data-Driven Control: Balanced Models with ERA

In this lecture, we connect the eigensystem realization algorithm (ERA) to balanced proper orthogonal decomposition (BPOD). In particular, if enough data is collected, then ERA produces...

Data-Driven Control: The Goal of Balanced Model Reduction

In this lecture, we discuss the overarching goal of balanced model reduction: Identifying key states that are most jointly controllable and observable, to capture the most input—output...

How the Flight Controller Code Works - dRehmFlight VTOL

This video will walk you through the flight controller code of dRehmFlight VTOL to give you a better understanding of the contents and structure. The hope is that it will cover almost...

Inner Products in Hilbert Space

This video will show how the inner product of functions in Hilbert space is related to the standard inner product of vectors of data.

SVD: Optimal Truncation [Matlab]

This video describes how to optimally truncate the singular value decomposition (SVD) for noisy data (Matlab code).

Stanford CS234: Reinforcement Learning | Winter 2019 | Lecture 15 - Batch Re...

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 5: Model Free Control

Dives into On Policy Monte-Carlo Control and Temporal Difference Learning, as well as Off-Policy Learning.

Ratio Control and Scaled Signal Calculations

When and how to use ratio, and how to implement within standard scaled signals

Data-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.

Dynamic 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.

Singular Value Decomposition (SVD): Matrix Approximation

This video describes how the singular value decomposition (SVD) can be used for matrix approximation.

Linear Regression 1 [Matlab]

This video describes how the singular value decomposition (SVD) can be used for linear regression in Matlab (part 1).

Stanford CS234: Reinforcement Learning | Winter 2019 | Lecture 9 - Policy Gr...

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.