Koopman Spectral Analysis (Representations)

In this video, we explore how to obtain finite-dimensional representations of the Koopman operator from data, using regression.

Stanford CS234: Reinforcement Learning | Winter 2019 | Lecture 7 - Imitation...

Professor Emma Brunskill

Assistant Professor, Computer Science

Stanford AI for Human Impact Lab

Stanford Artificial Intelligence Lab

Statistical Machine Learning Group

Singular Value Decomposition (SVD): Mathematical Overview

This video presents a mathematical overview of the singular value decomposition (SVD).

Stanford CS234: Reinforcement Learning | Winter 2019 | Lecture 5 - Value Fun...

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 3: Planning by Dynamic Programming

Introduces policy evaluation and iteration, value iteration, extensions to dynamic programming and contraction mapping.

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...

Data-Driven Control: Balanced Truncation

In this lecture, we describe the balanced truncation procedure for model reduction, where a handful of the most controllable and observable state directions are kept for the reduced-order...

Complex Fourier Series

This video will describe how the Fourier Series can be written efficiently in complex variables.

Sparse Identification of Nonlinear Dynamics for Model Predictive Control

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.

Stanford CS234: Reinforcement Learning | Winter 2019 | Lecture 6 - CNNs and ...

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

Introduces model-based RL, along with integrated architectures and simulation based search.

Numerically Linearizing a Dynamic System

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...

Data-Driven Control: Eigensystem Realization Algorithm

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...

Koopman Spectral Analysis (Overview)

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...

Stanford CS234: Reinforcement Learning | Winter 2019 | Lecture 11 - 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: 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...

Singular Value Decomposition (SVD): Dominant Correlations

This lectures discusses how the SVD captures dominant correlations in a matrix of data.

Stanford CS234: Reinforcement Learning | Winter 2019 | Lecture 8 - 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 2: Markov Decision Process

Explores Markov Processes including reward processes, decision processes and extensions.

Euler Angles and the Euler Rotation Sequence

In this video we discuss how Euler angles are used to define the relative orientation of one coordinate frame to another.

Data-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.

Extremum Seeking Control: Challenging Example

This lecture explores the use of extremum-seeking control (ESC) to solve a challenging control problem with a right-half plane zero.

The Frobenius Norm for Matrices

This video describes the Frobenius norm for matrices as related to the singular value decomposition (SVD).

Stanford CS234: Reinforcement Learning | Winter 2019 | Lecture 1 - Introduct...

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