• What Is Machine Learning?

    6,336 views 3 weeks ago
    Walk through the three types of machine learning (clustering, classification, and regression) in this overview by Loren Shure.
    3 Things you need to know about machine learning: http://bit.ly/2PG4qee

    In this video, you’ll get a summary of what machine learning is. You’ll start by learning about clustering, which helps you segment a collection of things into groups with distinct attributes. You’ll next explore classification, which you’d use for applications like object detection in images, predictive maintenance, and spam detection. Lastly, you’ll hear about regression, which is used to build models that predict a response along a continuum given other features.

    MATLAB for Machine Learning: http://bit.ly/2O9Sujp
    MATLAB for Deep Learning: http://bit.ly/2Dl0jm4


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  • Introduction to Deep Learning Play all

    Watch this series of MATLAB Tech Talks to explore key deep learning concepts. Learn to identify when to use deep learning, discover what approaches are suitable for your application, and explore some of the challenges you might encounter.
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  • Understanding Model Predictive Control Play all

    In this series, you'll learn how model predictive control (MPC) works, and you’ll discover the benefits of this multivariable control technique.

    MPC uses a model of the system to make predictions about the system’s future behavior. MPC solves an online optimization algorithm to find the optimal control action that drives the predicted output to the reference. MPC can handle multi-input multi-output systems that may have interactions between their inputs and outputs. It can also handle input and output constraints. MPC has preview capability; it can incorporate future reference information into the control problem to improve controller performance.

    This series also discusses MPC design parameters such as the controller sample time, prediction and control horizons, constraints, and weights. It also gives you recommendations for choosing these parameters. You'll learn about adaptive, gain-scheduled, and nonlinear MPCs, and you’ll get implementation tips to reduce the computational complexity of MPC and run it faster.

    Finally, the series demonstrates examples for designing MPC controllers in MATLAB® and Simulink®.
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  • Drone Simulation and Control Play all

    See a workflow for developing a control system that takes you from the basics of drone mechanics and to the test flight.You’ll learn about the sensors and actuators used in quadcopter control. You’ll also learn how to command a quadcopter’s four propellers in very specific ways that allow the drone to independently roll, pitch, yaw, and thrust.

    We’ll then build on that knowledge to design a control system architecture for hovering a quadcopter. That means, we’re going to figure out which states we need to feedback, how many controllers we need to build, and how those controllers interact with each other.

    We’ll review the quadcopter example in Simulink® and show how each component contributes to getting a quadcopter to hover safely. We’ll also walk through the nonlinear model of the drone and operating environment.

    Finally, by the end of this series, we’ll develop a linear model of the system and use that model to tune the PID controllers.
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  • Understanding Kalman Filters Play all

    Discover real-world situations in which you can use Kalman filters. Kalman filters are often used to optimally estimate the internal states of a system in the presence of uncertain and indirect measurements. Learn the working principles behind Kalman filters by watching some introductory examples.
    You will explore situations where Kalman filters are commonly used. When the states of a system can only be measured indirectly, then Kalman filter can be used to optimally estimate the states of the system. And when measurements from different sensors are available but subject to noise, Kalman filter is used to combine sensory data from various sources (known as sensor fusion) to find the best estimate of the parameter of interest.
    You will also learn about state observers by walking through some examples and simple math. This will help you understand what a Kalman filter is and how it works. At a high level, Kalman filters are a type of optimal state estimator. The videos include a discussion of nonlinear state estimators, such as extended and unscented Kalman filters.
    Finally, an example demonstrates how the states of a linear system can be estimated using Kalman filters, MATLAB®, and Simulink®.
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  • Understanding PID Control Play all

    This series provides an introduction to proportional-integral-derivative (PID) control.

    PID is just one form of feedback controller, and it can be fairly easy to understand and implement. It is the simplest type of controller that uses the past, present, and future error, and it’s these primary features that you need to satisfy most control problems. That is why PID is the most prevalent form of feedback control for a wide range of real applications.

    Often, when learning something new in control theory, it’s easy to get bogged down in the detailed mathematics of the problem. So in this series, we’re going to skip most of the math and instead focus on building a solid foundation.

    Throughout this series, you’ll learn what a PID controller is, how to modify it to make it more robust, and you’ll get an overview of tuning methods. Along the way, you’ll understand how PID controllers are used to handle practical applications like actuator saturation and the anti-windup algorithms that protect against it, sensor noise and the derivative filter that is required, and multi-loop control.
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  • Understanding Control Systems Play all

    Learn the basic concepts behind controls systems. Walk through everyday examples that outline fundamental ideas, and explore open-loop and feedback control systems.

    These videos explore open-loop systems that are found in everyday appliances like toasters or showers. The series illustrates how you can tune these systems using trial-and-error to achieve a desired output. You’ll also learn about situations where open-loop control may fail due to unexpected environmental changes (disturbances) or variations in the system.

    Next, you’ll explore the working principles behind feedback control, and discover how it deals with the shortcomings of open-loop control. Basic components of a feedback control system (such as “plants,” “actuators,” and “sensors”) are discussed, along with how these components interact with each other to form a closed-loop control system. You’ll discover how disturbances acting on the plant can affect system output in an undesired way, and how feedback control can compensate for such disturbances. The video series also discusses how noise can enter the system through measurement, which affects the measured output.

    Finally, you’ll learn to use MATLAB and Simulink to model and simulate some of the open-loop and feedback control systems introduced in this series.
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  • Understanding Bode Plots Play all

    MATLAB Tech Talks on Bode plot.
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  • Understanding Wavelets Play all

    Watch the videos in this series to learn the basics behind applications such as wavelet-based denoising and compression. You will learn fundamental concepts in wavelet analysis such as what wavelets are and how to scale and shift them. You will get an overview of the continuous and discrete wavelet transforms, and you will also see example applications of how to use these transforms in MATLAB®.
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  • Understanding Discrete-Event Simulation - MATLAB Tech Talks Play all

    Watch the videos in this MATLAB® Tech Talk series to learn the fundamentals behind discrete-event simulation. Discrete-event simulation is a simple, versatile way of describing a process. You can use it to build complex models that explore fundamental questions such as latency, utilization, and bottlenecks. The video series also outlines how to use stochastic processes to approximate details of a system that you can’t model. You’ll learn about how you can use discrete-event simulation for operations research, and you’ll explore how to use discrete-event simulation to evaluate the performance of digital communication systems.
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  • Understanding State Machines Play all

    Watch the videos in this series to learn the fundamental concepts of state machines. A state machine is a model that describes the behavior of a system in each state. It defines how the system should transition between these states when certain conditions are true. State machines are used to model logic in many dynamic systems such as automobiles, aircraft, robots, and mobile phones.
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