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Sometimes agents must learn how to associate certain conditions with actions and outcomes. In this course, you will learn some of the principles of machine learning and how to use it to make smarter agents.
Learning for Computers
start the course
describe how AI learns and the different types of machine learning
describe how examples can be used for learning
describe decision trees and how the model expresses knowledge
describe entropy and information gain for learning decision tree models
describe how to choose attributes to learn a decision tree
describe overfitting and how decision tree models can be made to mitigate this issue
describe neural networks and how they apply to artificial intelligence
describe the structure of a neural network and its individual neurons
list some of the common types of neural networks and what problems they might be good at solving
describe how machine learning works with a perceptron
describe how perceptron learning can be generalized to a multilayered neural network
describe convolutional neural networks
describe recurrent neural networks
Practice: Perceptron Training
describe how a perceptron can learn how to achieve a particular result given a set of examples
The materials within this course focus on the Knowledge Skills and Abilities (KSAs) identified within the Specialty Areas listed below. Click to view Specialty Area details within the interactive National Cybersecurity Workforce Framework.