Unit 3/Chapter 1 AI and ML

What is Machine Learning?

• A. A process where humans manually program computers to perform tasks.

• B. How computers recognize patterns and make decisions without being explicitly programmed.

• C. A method of creating physical models for scientific experiments.

• D. A technique for designing user interfaces.

Which of the following is NOT part of the Problem Solving Process in Machine Learning?

• A. Define the problem.

• B. Prepare the data.

• C. Train and test the model.

• D. Create a physical prototype.

What is a 'Model' in the context of Machine Learning?

• A. A physical representation of a concept.

• B. A computer program designed to make a decision.

• C. A set of rules for data entry.

• D. A type of database.

What is 'Unsupervised Learning'?

• A. Training a model with labeled data.

• B. Finding patterns in data that doesn’t have any labels.

• C. Using a model to predict future events.

• D. Manually programming a computer to perform tasks.

What are 'Features' in Machine Learning?

• A. The output you are trying to decide or predict.

• B. The inputs that the model uses to make decisions.

• C. The process of giving examples to a model so it can learn.

• D. The steps taken to prepare data.

What is 'Supervised Learning'?

• A. When a human trains a model to learn with examples.

• B. When a model learns without any human intervention.

• C. When a model is used to create physical prototypes.

• D. When a model is used to design user interfaces.

What is 'Training' in the context of Machine Learning?

• A. The process of giving examples to a model so it can learn.

• B. The final step in deploying a model.

• C. The process of defining the problem.

• D. The method of creating physical models.

What is a 'Label' in Machine Learning?

• A. The output you are trying to decide or predict.

• B. The input data used by the model.

• C. The process of training a model.

• D. The method of evaluating a model.

What is a "Mental Model" used for in the learning process?

• To represent and break down information into pieces

• To memorize information quickly

• To create physical models of concepts

• To ignore unnecessary information

What is an example of Unsupervised Learning mentioned in the text?

• Grouping similar numbers together to find patterns

• Teaching a computer to recognize handwriting

• Helping a computer learn with examples

• Using a mentor to teach new concepts