
Linear Regression 2 [Python]
This video describes how the singular value decomposition (SVD) can be used for linear regression in Python (part 2).
See MoreUsing 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 MoreStanford CS234: Reinforcement Learning | Winter 2019 | Lecture 10 - Policy G...
Professor Emma Brunskill
Assistant Professor, Computer Science
Stanford AI for Human Impact Lab
Stanford Artificial Intelligence Lab
Statistical Machine Learning Group
Smart Projectile State Estimation Using Evidence Theory
This journal article provides a very good practical understanding of Dempster-Shafer theory using sensor fusion and state estimation as the backdrop.
See MoreFreeFlyer Aerodynamic Simulation Software
FreeFlyer® is an aerodynamic simulation software for space mission design, analysis and operations. It is a commercial software used in actual missions. Free for students, paid for startups...
See MoreData-Driven Control: ERA and the Discrete-Time Impulse Response
In this lecture, we describe how the discrete-time impulse response is used in the eigensystem realization algorithm (ERA).
See MoreSVD: Eigenfaces 2 [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 2).
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 MoreUsing Antenna Toolbox with Phased Array Systems
When you create antenna arrays such as a uniform linear array (ULA), you can use antennas that are built into Phased Array System Toolbox™. Alternatively, you can use Antenna Toolbox™...
See MoreRL Course by David Silver - Lecture 1: Introduction to Reinforcement Learnin...
Introduces reinforcment learning (RL), an overview of agents and some classic RL problems.
See MoreAutomatic Updates of Transition Potential Matrices in Dempster-Shafer Networ...
Journal article that develops an evidential reasoning network capable of learning/updating the relationships between Frames of Discernment (the sets over which Dempster-Shafer reasons that...
See MoreData-Driven Control: Balanced Proper Orthogonal Decomposition
In this lecture, we introduce the balancing proper orthogonal decomposition (BPOD) to approximate balanced truncation for high-dimensional systems.
See More3D 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.
See MoreSVD: Optimal Truncation [Python]
This video describes how to optimally truncate the singular value decomposition (SVD) for noisy data (Python code).
See MoreTrimming a Model of a Dynamic System Using Numerical Optimization
In this video we show how to find a trim point of a dynamic system using numerical optimization techniques. We generate a cost function that corresponds to a straight and level flight...
See MoreStanford 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
RL Course by David Silver - Lecture 6: Value Function Approximation
A deep dive into incremental methods and batch methods of value function approximation.
See MoreFIR 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...
See MoreData-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...
See MoreDynamic 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...
See MoreSVD 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.
See MoreLinear Regression 1 [Python]
This video describes how the singular value decomposition (SVD) can be used for linear regression in Python (part 1).
See More