MATLAB Machine LearningApress, 28 Des 2016 - 326 halaman This book is a comprehensive guide to machine learning with worked examples in MATLAB. It starts with an overview of the history of Artificial Intelligence and automatic control and how the field of machine learning grew from these. It provides descriptions of all major areas in machine learning. The book reviews commercially available packages for machine learning and shows how they fit into the field. The book then shows how MATLAB can be used to solve machine learning problems and how MATLAB graphics can enhance the programmer’s understanding of the results and help users of their software grasp the results. Machine Learning can be very mathematical. The mathematics for each area is introduced in a clear and concise form so that even casual readers can understand the math. Readers from all areas of engineering will see connections to what they know and will learn new technology. The book then providescomplete solutions in MATLAB for several important problems in machine learning including face identification, autonomous driving, and data classification. Full source code is provided for all of the examples and applications in the book. What you'll learn:
Who is this book for: The primary audiences are engineers and engineering students wanting a comprehensive and practical introduction to machine learning. |
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100 Time sec acceleration activation functions adaptive control aircraft algorithm angle automobile autonomous learning cell array chapter coefficient compute control system covariance create d.name d.parent d.row Damping ratio datastore decision tree default data structure digits dRHS dynamics end end equations eventdata example function Demo GLPK handles hypotheses identify implement Initialize input kInNRow layer learning control linear machine learning mapreduce MATLAB matrix measurement MRAC nargin nargout neural net neural network Neuron node noise nonlinear optimization oscillator output Particle Filter PID controller pitch pixels PlotSet Problem We want pruning radar recursive return end RHSOscillator RungeKutta scan script shown in Figure sigma points sigmoid sigmoid function simulation SNOPT Softmax step struct Tall Arrays Toolbox track-tree tracks training set Unscented Kalman Filter unsupervised learning update varargin{k+1 variable vector velocity weights xDot xlabel xPlot zero