Numerically Linearizing a Dynamic System
104 min
Intermediate
Video
Theory
In this video we show how to linearize a dynamic system using numerical techniques. In other words, the linearization process does not require an analytical description of the system. This...
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Teaching resources for a reinforcement learning course
Intermediate
Course
Theory
Teaching resources by Dimitri P. Bertsekas for reinforcement learning courses. The website has links for freely available textbooks (for instructional purposes), videolectures, and course...
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Data-Driven Control: The Goal of Balanced Model Reduction
15 min
Intermediate
Video
Theory
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...
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Singular Value Decomposition (SVD): Dominant Correlations
11 min
Intermediate
Video
Theory
This lectures discusses how the SVD captures dominant correlations in a matrix of data.
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Stanford CS234: Reinforcement Learning | Winter 2019 | Lecture 8 - Policy Gr...
71 min
Intermediate
Video
Theory
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 2: Markov Decision Process
102 min
Intermediate
Video
Theory
Explores Markov Processes including reward processes, decision processes and extensions.
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Euler Angles and the Euler Rotation Sequence
70 min
Intermediate
Video
Theory
In this video we discuss how Euler angles are used to define the relative orientation of one coordinate frame to another.
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Data-Driven Control: Balanced Proper Orthogonal Decomposition
27 min
Intermediate
Video
Theory
In this lecture, we introduce the balancing proper orthogonal decomposition (BPOD) to approximate balanced truncation for high-dimensional systems.
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Extremum Seeking Control: Challenging Example
8 min
Intermediate
Video
Theory
This lecture explores the use of extremum-seeking control (ESC) to solve a challenging control problem with a right-half plane zero.
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The Frobenius Norm for Matrices
2 min
Intermediate
Video
Theory
This video describes the Frobenius norm for matrices as related to the singular value decomposition (SVD).
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Stanford CS234: Reinforcement Learning | Winter 2019 | Lecture 1 - Introduct...
66 min
Intermediate
Video
Theory
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 7: Policy Gradient Methods
94 min
Intermediate
Video
Theory
Looks at different policy gradients, including Finite Difference, Monte-Carlo and Actor Critic.
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Data-Driven Control: Change of Variables in Control Systems (Correction)
2 min
Intermediate
Video
Theory
This video corrects a typo in the previous lecture.
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Koopman Spectral Analysis (Multiscale systems)
5 min
Intermediate
Video
Theory
In this video, we discuss recent applications of data-driven Koopman theory to multi-scale systems.
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Stanford CS234: Reinforcement Learning | Winter 2019 | Lecture 10 - Policy G...
81 min
Intermediate
Video
Theory
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
Intermediate
Peer Reviewed Paper
Theory
This journal article provides a very good practical understanding of Dempster-Shafer theory using sensor fusion and state estimation as the backdrop.
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The Navigation Equations: Computing Position North, East, and Down
52 min
Intermediate
Video
Theory
In this video we show how to compute the inertial velocity of a rigid body in the vehicle-carried North, East, Down (NED) frame. This is achieved by rotating the velocity expressed in the...
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Manipulating Aerodynamic Coefficients
25 min
Intermediate
Video
Theory
In this video we discuss some potential problems you may encounter when attempting to perform operations with dimensionless aerodynamic coefficients such as CL and CD.
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Data-Driven Control: Observer Kalman Filter Identification
12 min
Intermediate
Video
Theory
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...
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Randomized SVD: Power Iterations and Oversampling
4 min
Intermediate
Video
Theory
This video discusses the randomized SVD and how to make it more accurate with power iterations (multiple passes through the data matrix) and oversampling.
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RL Course by David Silver - Lecture 1: Introduction to Reinforcement Learnin...
88 min
Intermediate
Video
Theory
Introduces reinforcment learning (RL), an overview of agents and some classic RL problems.
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Computing Euler Angles: The Euler Kinematical Equations and Poisson’s Kinema...
79 min
Intermediate
Video
Theory
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...
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Using Antenna Toolbox with Phased Array Systems
Intermediate
Example
Theory
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™...
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Data-Driven Control: Balanced Models with ERA
6 min
Intermediate
Video
Theory
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...
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SVD and Optimal Truncation
11 min
Intermediate
Video
Theory
This video describes how to truncate the singular value decomposition (SVD) for matrix approximation.
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