a set of examples used to teach a machine learning model how to recognise patterns

the output that a model generates based on the patterns determined from the training data

a measure of how likely the output of a model is correct

to disproportionately favour one side, group, or outcome over others

a data point that significantly differs from the rest of the data in a data set

British English CC

Testing a model

Training and testing a model

Computing

AI and machine learning applications

Gathering data for a model

Improving a model

Building a data-driven application

Documenting and evaluating a machine learning model

This unit is designed to develop pupils programming skills in a new engaging context: physical computing, using the BBC micro:bit. They will get acquainted with the host of components built into the micro:bit, and write simple programs that use these components to interact with the physical world.

Machine learning using the micro:bit

a set of examples used to teach a model how to recognise patterns

What is training data in machine learning?

A classifier uses {{ }} in a model to predict which class new data belongs to.

a data point that is significantly different from the rest of the data

a value that is exactly the same as all others

the most frequently occurring value in the data set

a missing value that does not affect model performance

a machine learning algorithm that groups data into categories

a system that erases training data after use

a way to manually sort data into a spreadsheet

What is a classifier in machine learning?

a set of examples used to teach an AI model

the model's output based on recognised patterns

a data point that differs significantly from the rest

to see if the computer makes mistakes on purpose

to ensure the AI can adapt to different movement styles and reduce bias

to collect random results without a clear purpose

Why is it important to test a model with different people?

to ensure data is consistent and reduce variability

to randomly change the motion values for more variety

Why is it important to hold the micro:bit in the same position when recording movement samples?

A {{ }} approach involves using patterns in data rather than predefined rules.

Which axis changes the most when moving a device side to side?

A {{ }} is a collection of similar data samples grouped together under a label.

A data-driven system requires a large number of {{ }} to learn patterns.

to introduce random variations in the data set

to make sure the model identifies consistent patterns

so the computer has more numbers to store

Why do we need to collect multiple samples for a model?

Starter Quiz

Why is it important to hold the micro:bit in the same position when recording movement samples?