Types of Machine Learning

Front

What are the three types of machine learning?

Back

Supervised learning, Unsupervised learning, Reinforcement learning

Front

Why is there a need for both training and test data in machine learning?

Back

To ensure the model can generalize beyond the training data.

Front

Which type of machine learning are you using when training a model with labeled images of apples and tomatoes?

Back

Supervised learning

Answer explanation

Training a model with labeled images of apples and tomatoes falls under supervised learning where the algorithm learns from labeled data to make predictions.

Front

What is done with the remaining data after a machine learning model is trained?

Back

It is used to test the model

Answer explanation

The remaining data after training is used to test the model's performance and evaluate its accuracy.

Front

What is bias in the context of machine learning models?

Back

When the model favours some things and deprioritises or excludes others.

Answer explanation

Bias in machine learning models refers to favouring some things while deprioritizing or excluding others, leading to unfairness or inaccuracies. e.g A facial recognition system that is less accurate in recognising people with certain skin tones

Front

Why might an AI application that predicts jobs based on employment data from 1960 exhibit societal bias?

Back

Because the data from 1960 reflects biases that existed in society at that time.

Answer explanation

The correct choice is because the data from 1960 reflects biases that existed in society at that time, which can lead to societal bias in the AI application.

Front

Can societal bias lead to data bias?

Back

Yes, it can

Answer explanation

Societal bias can influence the data collected, leading to biased results. Therefore, societal bias can indeed lead to data bias.

Front

When designing a machine learning application, whose backgrounds, experiences, and opinions should be considered to reduce bias?

Back

People with different backgrounds, experiences, and opinions

Front

Why is it important to use large and representative sets of data to train ML models?

Back

To reduce bias in the model

Answer explanation

Using large and representative sets of data helps reduce bias in the model by capturing a wider range of patterns and relationships, leading to more accurate predictions.