3D Printed Laboratory Equipment to Study Fundamentals of Vibrations: Complia...

This low-cost, portable, and 3D-Printed Laboratory Equipment (3D-PLE) can be utilized to achieve the following learning outcomes: 

• Derive the equation of motion of a translational...

Randomized Singular Value Decomposition (SVD)

This video describes how to use recent techniques in randomized linear algebra to efficiently compute the singular value decomposition (SVD) for extremely large matrices.

SVD: Optimal Truncation [Python]

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

Computing Euler Angles: The Euler Kinematical Equations and Poisson’s Kinema...

In this video we discuss how the time rate of change of the Euler angles are related to the angular velocity vector of the vehicle. This allows us to design an algorithm to consume...

RL Course by David Silver - Lecture 1: Introduction to Reinforcement Learnin...

Introduces reinforcment learning (RL), an overview of agents and some classic RL problems.

RL Course by David Silver - Lecture 6: Value Function Approximation

A deep dive into incremental methods and batch methods of value function approximation.

FIR Filter Design and Software Implementation

FIR (Finite Impulse Response) filter theory, design, and software implementation. Real-time software implementation on a custom STM32-based PCB. Overview of digital filtering, use-cases...

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).

Derivation of Rodrigues’ Rotation Formula

In this video we explain and derive Rodrigues’ Rotation Formula. This functions describes how to rotate an arbitrary vector about another arbitrary axis of rotation. This has applications to...

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

NASA's General Mission Analysis Tool (GMAT)

NASA's GMAT is the worlds only enterprise, multi-mission, open source software system for space mission design, optimization, and navigation. The system supports missions in flight regimes...

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.

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).

A Nonlinear, 6 DOF Dynamic Model of an Aircraft: the Research Civil Aircraft...

In this video we develop a dynamic model of an aircraft by describing forces and moments generated by aerodynamic, propulsion, and gravity that act on the aircraft. This video outlines the...

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...

An efficient orientation filter for inertial and inertial/magnetic sensor ar...

This report presents a novel orientation filter applicable to IMUs consisting of tri-axis gyroscopes and accelerometers, and MARG sensor arrays that also include tri-axis magnetometers. The...

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...

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).