Day of AI 2024: 3-5

3-5.DA.9 Use data to highlight and/or propose relationships, predict outcomes, or communicate ideas.

In addition to the above standard, this activity relates to Practice 4. Developing and Using Abstractions of the K–12 Computer Science Framework as students identify common features in a dataset.

Processing (Sensing vs Perception)
3-5.1-B-i​​ Use a software tool such as a speech transcription or visual object recognition demo to demonstrate machine perception, and explain why this is perception rather than mere sensing.

Representation (Feature vectors)
K-2.2-A-iv Identify the features that make each object in a collection unique. and create a table of features to organize the objects. 

Nature of Learning (Finding patterns in data)
K-2.3-A-ii Identify patterns in labeled data and determine the features that predict labels.

Nature of Learning (Training a Model) 

3-5.3-A-iii Train a classification model using machine learning, and then examine the accuracy of the model on new inputs.

Nature of Learning (Constructing vs. using a reasoner)

3-5.3-A-iv Demonstrate how training data are labeled when using a machine learning tool.

Datasets (Feature sets)
3-5.3-C-i Create a labeled dataset with explicit features of several types and use a machine learning tool to train a classifier on this data.

Datasets (Large datasets)

3.5.3-C-ii  Illustrate how training a classifier for a broad concept such as "dog" requires a large amount of data to capture the diversity of the domain.