Titlebook: MATLAB Machine Learning Recipes; A Problem-Solution A Michael Paluszek,Stephanie Thomas Book 2024Latest edition Michael Paluszek and Stepha

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An Overview of Machine Learning,ern recognition, computational statistics, and artificial intelligence. The data may be historical or updated in real time. Machine learning is important in areas like facial recognition, spam filtering, content generation, and other areas where it is not feasible, or even possible, to write algorit

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Kalman Filters,stem. A model can be a predefined structure or can be determined solely through data. In the case of Kalman Filtering, we create a model and use the model as a framework for learning about the state of the system.

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Classification of Numbers Using Neural Networks,ch fall into the Machine Learning branch of our Autonomous Learning taxonomy. In this case, we will look at images of computer-generated digits and the problem of identifying the digits correctly. These images will represent numbers from scanned documents. Attempting to capture the variation in digi

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Data Classification with Decision Trees, in our Autonomous Learning taxonomy. Binary trees are easiest to implement because each node branches to two other nodes, or none. We will create functions for the decision trees and to generate sets of data to classify. Figure 10.1 shows a simple binary tree. Point “a” is in the upper-left quadran

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Multiple Hypothesis Testing,o track aircraft. Aircraft in flight must track all nearby objects to avoid collisions and to determine if they are threats. Automobiles with radar cruise control use their radar to track cars in front of them so that the car can maintain safe spacing and avoid a collision.

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