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Unit 7: AI & Machine Learning (lessons 1-6)

Total questions: 27

Worksheet time: 14mins

Name
Class
Date
1.

How computers recognize patterns and make decisions without being explicitly programmed is known as

a)

Machine Learning

b)

Bias

c)

Training

2.

When a decision favors some things and de-prioritizes or excludes others is known as

a)

Machine Learning

b)

Bias

c)

Training

3.

Giving examples to a model so it can learn is known as

a)

Machine Learning

b)

Bias

c)

Training

4.

Which stage of the Design Process are we in when we state the problem we are solving?

a)

Define

b)

Prepare

c)

Try

d)

Reflect

5.

Which stage of the Design Process are we in when we decide what data we want to use?

a)

Define

b)

Prepare

c)

Try

d)

Reflect

6.

Which stage of the Design Process are we in when we train and test a model?

a)

Define

b)

Prepare

c)

Try

d)

Reflect

7.

Which stage of the Design Process are we in when we determine how well the model did and how it an be improved?

a)

Define

b)

Prepare

c)

Try

d)

Reflect

8.

When we are creating an AI model, it is important to examine its impact on society.

a)

True

b)

False

9.

The inputs that a model uses to make decision are known as

a)

Features

b)

Model

c)

Label

10.

The output you are trying to decide or predict with a model is known as a

a)

Features

b)

Model

c)

Label

11.

A computer program designed to make a decision is known as a

a)

Features

b)

Model

c)

Label

12.

Using this model to predict house prices, the number of bedrooms, square footage, and neighborhood are all examples of

a)

features

b)

labels

13.

Using this model to predict house prices, $250,000 is an example of a

a)

feature

b)

label

14.

When a human trains a model to learn with examples it is known as

a)

supervised learning

b)

unsupervised learning

15.


Finding patterns in data that doesn't have any labels is known as

a)

supervised learning

b)

unsupervised learning

16.

In the Green Glass Door App, the Wizard shows us which words are accepted and rejected. In essences, the wizard is training us to figure out the letter patterns of the words that will be accepted. This is an example of,

a)

Supervised Learning

b)

Unsupervised Learning

17.

In the Looking for Patterns number App, we have to move the numbers around in whatever pattern we see. This is an example of,

a)

Supervised Learning

b)

Unsupervised Learning

18.

You see someone playing your favorite song on a piano. After they leave, you sit at the piano and press random notes on the piano until you start to recreate the melody from the song. This is an example of,

a)

Supervised Learning

b)

Unsupervised Learning

19.

You go to a swim coach who helps you learn how to swim in a pool. You watch them demonstrate basic strokes before practicing on your own. This is an example of,

a)

Supervised Learning

b)

Unsupervised Learning

20.

The elders in your family show you how to cook meals from recipes passed down from generation to generation. You read the recipe and watch them cook the meal first before you try making it yourself. This is an example of,

a)

Supervised Learning

b)

Unsupervised Learning

21.

A toddler watches all of the adults in their life walk around the house. Eventually, after a lot of falling and stumbling, they learn to find their balance and start walking. This is an example of,

a)

Supervised Learning

b)

Unsupervised Learning

22.

You know that there are spices in your kitchen, but you’re not sure what to do with them. You add little bits of different spices and taste as you go until you settle on a taste that you like. This is an example of,

a)

Supervised Learning

b)

Unsupervised Learning

23.

You study different vocabulary words for science class by creating flash cards. On one side is the word and the other side is the definition. As you study, you gradually start to memorize the terms. This is an example of,

a)

Supervised Learning

b)

Unsupervised Learning

24.

What is data that can be separated into groups?

a)

Categorical Data

b)

Numerical Data

c)

Classification

d)

Ethics

25.

What is predicting a category based on other features?

a)

Categorical Data

b)

Numerical Data

c)

Classification

d)

Ethics

26.

What is data that can be counted or measured?

a)

Categorical Data

b)

Numerical Data

c)

Classification

d)

Ethics

27.

What are guidelines for good behavior?

a)

Categorical Data

b)

Numerical Data

c)

Classification

d)

Ethics