RL Course by David Silver - Lecture 5: Model Free Control

Dives into On Policy Monte-Carlo Control and Temporal Difference Learning, as well as Off-Policy Learning.

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RL Course by David Silver - Lecture 10: Classic Games

An overview of Game Theory, minimax search, self-play and imperfect information games.

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Direct Synthesis for PID Design Intro

Direct Synthesis for PID Design Intro

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Introduction to Ordinary Differential Equations

In this video we introduce the concept of ordinary differential equations (ODEs). We give examples of how these appear in science and engineering as well as...

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Cartesian, Polar, Cylindrical, and Spherical Coordinates

In this video we discuss Cartesian, Polar, Cylindrical, and Spherical coordinates as well as develop forward and reverse transformations to go from one coord...

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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...

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Understanding Sensor Fusion and Tracking, Part 1: What Is Sensor Fusion?

This video provides an overview of what sensor fusion is and how it helps in the design of autonomous systems. It also covers a few scenarios that illustrate the various ways that sensor...

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Time Domain Analysis with Matlab: Using the Linear System Analyzer

In this video we explore various Matlab functions and workflows to perform time domain analysis of a dynamic system. This includes the use of ‘tf’, ‘step’, ...

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Peter Ponders PID - Feed Forward Theory and Calculations

The formulas for feed forwards are derived for motors and hydraulic actuators. See More

Control Bootcamp: Benefits of Feedback on Cruise Control Example (Part 2)

Here we investigate the benefits of feedback for systems with uncertain dynamics and disturbances, as illustrated on a cruise control example. (Part 2)

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Derivation and Solution of Laplace’s Equation

In this video we show how the heat equation can be simplified to obtain Laplace’s equation. We investigate how to solve Laplace’s equation using separation ...

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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...

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The Fourier Transform

This video will discuss the Fourier Transform, which is one of the most important coordinate transformations in all of science and engineering.

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Frequency domain – tutorial 1: concept of frequency (with Chinese subtitle)

In this video, the following materials are covered:1) intuitive explanation on the frequency concept 2) what is the relation between time and frequency domai...

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Randomized SVD: Power Iterations and Oversampling

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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Lecture 29: State space representation

This video explains how a system can be modelled in state space form. Also shown is how to obtain transfer function from State space form See More

Frequency domain – tutorial 5: Fourier transform

In this video, we learn about Fourier transform which enables us to travel from time to frequency domain when a signal is not periodic. The learning objectiv...

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Control Bootcamp: Three Equivalent Representations of Linear Systems

This video explores three equivalent representations of linear systems: State-space ODEs, Frequency domain transfer functions, and Time-domain impulse response convolution.

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Bode Stability Criterion in Frequency Response Analysis Intro

The Bode stability criterion allows us to quickly determine the stability and relative stability of a transfer function. It uses a graphical method that can ...

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Lecture 19: Lead and PD compensator Design using Root Locus

This video discusses how a Proportional Derivative [PD] controller and a Lead compensator can be designed using Root Locus See More

Stanford CS234: Reinforcement Learning | Winter 2019 | Lecture 4 - Model Fre...

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

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Machine Learning Goals

This lecture discusses the high-level goals of machine learning, and what we want out of our models. Goals include speed and accuracy, along with interpretability, generalizability...

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Feedforward Control Intro

If we know how a disturbance will affect an output, we can proactively change our manipulated variable to counteract it.

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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.

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Introduction to System Stability and Control

This video attempts to provide an intuitive understanding of concepts like stability and stability margin. I briefly describe both of these topics with examples and explain how you can...

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