Sparse Identification of Nonlinear Dynamics for Model Predictive Control
12 min
Intermediate
Video
Theory
This lecture shows how to use sparse identification of nonlinear dynamics with control (SINDYc) with model predictive control to control nonlinear systems purely from data.
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Stanford CS234: Reinforcement Learning | Winter 2019 | Lecture 6 - CNNs and ...
80 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 8: Integrating Learning and Planning
100 min
Intermediate
Video
Theory
Introduces model-based RL, along with integrated architectures and simulation based search.
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Velocity & Acceleration in Non-Inertial Reference Frames (Coriolis &...
133 min
Intermediate
Video
Theory
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...
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Data-Driven Control: Eigensystem Realization Algorithm
12 min
Intermediate
Video
Theory
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...
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Koopman Spectral Analysis (Overview)
27 min
Intermediate
Video
Theory
In this video, we introduce Koopman operator theory for dynamical systems. The Koopman operator was introduced in 1931, but has experienced renewed interest recently because of the...
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Stanford CS234: Reinforcement Learning | Winter 2019 | Lecture 11 - Fast Rei...
79 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
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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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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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: 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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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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