Lesson 3: Bias in, bias out

Create a machine learning model to classify images and explore how a limited data set can lead to bias.

Ishodi učenja

Describe the impact of data on the accuracy of a machine learning (ML) model
Explain the need for both training and test data
Explain how bias can influence the predictions generated by an ML model

Ključni pojmovi

Artificial intelligence (AI), machine learning (ML), supervised learning, classification, training data, test data, accuracy, bias, data bias, societal bias

Struktura scenarija poučavanja

The three different types of machine learning
Supermarket AI application
Training a model
Bias
Student timetable model
Reducing bias

Pregled scenarija poučavanja

Dozvoliti YouTube sadržaj?

Upotrebljavamo YouTube da bismo vam prikazali videozapise na našoj internetskoj stranici. Tražimo vašu dozvolu prije nego učitamo sadržaj jer YouTube možda upotrebljava kolačiće za pomoć pri praćenju upotrebe i poboljšavanje svojih usluga.

Možda biste htjeli pročitati sljedeće Googleove dokumente prije prihvaćanja: Politika kolačića i Politika privatnosti.