What is Machine Learning Introduction Well-Posed Learning Problems Designing a Learning System Steps for Designing Learning System Are Perspectives and Issues in Machine Learning Concept Learning and the General to Specific Ordering Concept Learning Task Concept Learning as a Search Important Representation Inductive Bias

Decision Tree Learning Types of Decision Tree Strengths and Weakness of Decision Tree Approach Appropriate Problems in Decision Tree Learning Issues in Decision Tree Learning What is Hypothesis Types of Hypothesis ID3 Information Gain for Each Feature

Artificial Neural Networks I Biological Motivation Neural Network Representations Appropriate Problems for Neural Network Learning Perceptron Genetic Algorithms Application Areas What is Reinforcement Learning Terms Used in Reinforcement Learning Approaches to Implement Reinforcement Learning Temporal Difference Learning EM Algorithm GIBS Algorithm

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Appropriate Problems for Neural Network Learning

ANN learning is well-suited to problems in which the training data corresponds to noisy, complex sensor data, such as inputs from cameras and microphones.
The BACKPROPAGATION algorithm is the most commonly used ANN learning technique.
ANN learning is well-suited to problems in which the training data corresponds to noisy, complex sensor data, such as inputs from cameras and microphones.
The BACKPROPAGATION algorithm is the most commonly used ANN learning technique.

It is appropriate for problems with the following characteristics:

Instances are represented by many attribute-value pairs.
The target function output may be discrete-valued, real-valued, or a vector of several real- or discrete-valued attributes.
The training examples may contain errors.
Long training times are acceptable.
Fast evaluation of the learned target function may be required.
The ability of humans to understand the learned target function is not important.

It is appropriate for problems with the following characteristics:

Instances are represented by many attribute-value pairs.
The target function output may be discrete-valued, real-valued, or a vector of several real- or discrete-valued attributes.
The training examples may contain errors.
Long training times are acceptable.
Fast evaluation of the learned target function may be required.
The ability of humans to understand the learned target function is not important.

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