Course Description
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 Objectives
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
Decision Trees
- 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
Neural Networks
- 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