The basic design issues and approaches to machine learning are illustrated by designing a program to learn to play checkers, with the goal of entering it in the world checkers tournament
1. Choosing the Training Experience
2. Choosing the Target Function
3. Choosing a Representation for the Target Function
4. Choosing a Function Approximation Algorithm
1. Estimating training values
2. Adjusting the weights
5. The Final Design
1. Choosing the Training Experience
- The first design choice is to choose the type of training experience from which the system will learn.
- The type of training experience available can have a significant impact on success or failure of the learner.
There are three attributes which impact on success or failure of the learner
1. Whether the training experience provides direct or indirect feedback regarding the choices made by the performance system
2. The degree to which the learner controls the sequence of training examples
3. How well it represents the distribution of examples over which the final system performance P must be measured
2. Choosing the Target Function
The next design choice is to determine exactly what type of knowledge will be learned and how this will be used by the performance program.
3. Choosing a Representation for the Target Function
4. Choosing a Function Approximation Algorithm
5. The Final Design
The final design of checkers learning system can be described by four distinct program modules that represent the central components in many learning systems