Living and Working with
Artificial Intelligence 

Learning Objectives

• List some different types of reasoning computers can do

• Determine what kind of reasoning is involved in a given computer application

Slides

1. View in Google Slides 

Learning Objectives

• Explain how to use a decision tree to classify an object.

• Demonstrate how to extend a decision tree to include a new class.

Mod 3.2 - Video Case Study

• Videos and Resources

Slides

1. View in Google Slides 

Learning Objectives

• Explain how decision tree classifiers work. (They should have gotten this from Pasta Land but it’s reinforced here.)

• Show how training examples can be encoded as feature vectors.

• Use MachineLearningForKids to set up and solve a machine learning problem.

Slides

1. View in Google Slides

Learning Objectives

• Explain how decision tree classifiers work. (They should have gotten this from Pasta Land but it’s reinforced here.)

• Show how training examples can be encoded as feature vectors.

• Use MachineLearningForKids to set up and solve a machine learning problem.

Slides

1. View in Google Slides

Learning Objectives

• Use AI Lab to explore a dataset.

• Train a classifier or predictor using AI Lab.

• Measure the performance of a classifier or predictor on a test set.

• Explain why running the same experiment in AI Lab multiple times may produce different results.

Slides

1. View in Google Slides

Learning Objectives

• Understand what tasks neural networks can be used for.

• Explain in basic terminology the operation of a single computing unit (neuron).

Mod 3.5 - Video Case Study

• Videos and Resources

Slides

1. View in Google Slides

Learning Objectives

• Understand how a linear threshold neuron works

• Understand how to solve problems by adjusting a neuron's wieghts and threshold

Mod 3.5 - Video Case Study

• Videos and Resources

Slides

1. View in Google Slides

2. Neuron Sandbox web site

Learning Objectives

• Identify current uses of AI to make decisions and how it has impacted society or people

• Evaluate the ways various stakeholders' values influence the design of an AI System 

• Identify and explain sources of bias in an AI system and decision making process 

• Explain how and why issues of bias, fairness, and transparency affect the design and use of AI systems and impacts on people

• Create example laws, policies, and procedures that regulate the design and use of AI Systems that make decisions about people.

Slides

1. View in Google Slides

Learning Objectives

• Understand what makes an AI biased and how to limit bias. 

• Build AI models and systems that are fair, ethical, and, transparent.

• Consider the users of your AI system.

Slides

1. View in Google Slides