Features, Parameters, and Patterns

Lesson goals

• Features describe inputs
• Parameters change during training
• Correlation is not always causation

Core idea

How a model turns input properties into decisions or predictions.

1. Features describe inputs: understand it in context, identify inputs and outputs, and define how the result will be verified.

2. Parameters change during training: understand it in context, identify inputs and outputs, and define how the result will be verified.

3. Correlation is not always causation: understand it in context, identify inputs and outputs, and define how the result will be verified.

Practical example

Consider a real application of “Features, Parameters, and Patterns.” Start small, identify available data, and compare the system with a clear baseline before expanding it.

Check your understanding

1. Explain “Features, Parameters, and Patterns” in your own words.
2. Give one appropriate and one misleading application.
3. What risk or limitation should be monitored?