AI - How computers learn from Data

Multiple Choice

What types of inputs can be used to train a Teachable Machine model?

• numbers, colors, and shapes

• text, videos, and gestures

• images, sounds, and poses

• smells, tastes, and temperatures

Multiple Choice

Picture a toddler. The child knows what the family cat looks like (provided they have one) but has no idea that there are a lot of other cats in the world that are all different. The thing is, if the kid sees another cat, he or she will still be able to recognize it as a cat through a set of features such as two ears, four legs, a tail, fur, whiskers, etc.

• Supervised Learning

• Unsupervised Learning

Multiple Choice

What are some uses of a 'smart' speaker?

• Interpreting voice commands

• Answering questions

• Playing music

• Setting alarms

Multiple Choice

Is a ‘smart’ speaker an AI application? Why?

• Yes

• No

• Sometimes

• Not sure

Multiple Choice

What is Machine Learning?

• Machine Learning is a subset of artificial intelligence

• Machine Learning is a physical machine that learns on its own
• Machine Learning is a form of traditional programming
• Machine Learning is a type of human learning process

Multiple Choice

What is machine learning and how does it relate to models?

• A method of programming rules

• A way to build AI systems

• A representation of real-world context

• A type of data analysis

Multiple Choice

What is the relationship between AI and machine learning?

• Machine learning is a subset of AI

• AI is a subset of machine learning

• AI and machine learning are the same

• Machine learning does not relate to AI

Multiple Choice

How do models ‘learn’?

• By defining exact steps

• By solving complex problems

• By making predictions

• By following rules

Multiple Choice

What is the primary goal of data science?

• Automating tasks

• Extracting meaningful insights from data

• Enhancing Computer Vision

• Developing mkachine learning algorithms

Multiple Choice

How does machine learning differ from traditional programming in terms of rules?

• Machine learning infers rules from data and labels, while traditional programming defines rules in a programming language.

• Machine learning defines rules in a programming language, while traditional programming infers rules from data and labels.

• Machine learning eliminates the need for rules, while traditional programming heavily relies on defined rules.

• Machine learning and traditional programming both use predefined rules.

Multiple Choice

Which of the following activities would you use machine learning for?

• Calculating the distance to a star

• Forecasting air pollution

• Identifying animals in a video

• None of the above

Multiple Choice

Recognizing ___ in data is an important part of machine learning.

• text

• patterns

• images

Multiple Choice

What data would you need to create a machine learning model for forecasting air pollution?

• Historical pollution data

• Weather data

• Traffic data

• Geographical data

Multiple Choice

What data would you need to create a machine learning model for identifying animals in a video?

• Images of animals

• Videos of animals

• Sound recordings of animals

• Text descriptions of animals

Multiple Select

What are the three types of machine learning mentioned in the image?

• Supervised learning

• Unsupervised learning

• Reinforcement learning

• Deep learning

Multiple Choice

What is the primary difference between supervised and unsupervised learning?

• Supervised learning uses labeled data, while unsupervised learning uses unlabeled data.

• Supervised learning is used only for classification problems, while unsupervised learning is used for regression problems.

• Unsupervised learning algorithms are always faster than supervised learning algorithms.

• Supervised learning can't handle big data, while unsupervised learning is designed for it.