AI/ML Literacy And Training ML Models Using Web Browsers

ASU Center for Blockchain Innovation and Emerging Technology

Albany State University, GA

Summer 2025


Week 2 (June 9- June 13)

General Dscription: In this week, we will introduce a mininum content of JavaScript that will be used to train a model using TensorFlow. Then, we will discuss a bit about linear regression, logistic regression, supervised and unsupervised machine learning.

Theme of the week: A bit about JavaScript, TensorFlow.js and machine learning

Weekly Schedule - Week 2

Week Day
Middle School Group
High School Group
Monday
  • Morning:
    Netflix problem – Using POE/MS 360 copilot to create a webpage of the Netflix recommender system problem

  • Afternoon:
    Discussions about linear regression
    Datasets, features and labels
    Library of datasets, Kaggle.com
    Regression models, cost functions
    Training dataset and test dataset
    Confusion matrix
Tuesday
  • Morning:
    Using POE to
    Teach and learn those areas that are weak during the pre-test
    Create webpage that covers related materials

  • Afternoon:
    Create a post-test that covers contents in part 1
  • Morning:
    Using VSC/OpenAI ChatGPT to create a webpage that train a ML regression model.
    Load a small diabetes dataset from Github
    Visualize the features inside the dataset

  • Afternoon:
    Select the features
    Select the cost function
    building a model, i.e., a linear equation of multiple variables.
    Split the dataset into training dataset and test dataset
    Training the model using the training dataset
    Test the trained model
    Calculate confusion matrix
Wednesday
  • Morning:
    Practice – part 1
    Go to kapple.com to select a dataset
    Download the dataset and upload to your github account
    Use AI to create a webpage

  • Afternoon:
    Visualize the dataset
    Complete the task above
Thursday
  • Morning:
    Using POE
    Teach and learn those areas that are weak during the pre-test
    Create webpage that covers related materials

  • Afternoon:
    Teach and learn those areas that are weak during the pre-test
    Create webpage that covers related materials
  • Morning:
    Practice - part 2 Use AI to add code in the webpage to
    display correlation charts
    Select features based on correlation charts

  • Afternoon:
    Choose the cost function
    Build a model
    Split the dataset to training part and test part
Friday
  • Morning:
    Using POE
    Teach and learn those areas that are weak during the pre-test
    Create webpage that covers related materials

  • Afternoon:
    Create a post-test that covers contents in part 2
  • Morning:
    Practice – part 3 Use AI to add code in the webpage to
    Train the model
    Test the trained model
    Calculate confusion matrix

  • Afternoon:
    Complete the task
    Complete a survey
Reading materials
1. Sixth Grade Math Contents
2. Prompts for generating webpage for part 1
3. The webpage generated by AI
1. Brief of Machine Learning
2. Prompts for generating webpage for model 1
1. The training model 1 by AI
Work completed
1. Austin, Jaiden
2. Austin, Jorden
3. Cahill, Amanda
4. Figgins, Jaleixia
5. Godwin, Garret
6. Lewis, Jeriamiah
7. Jackson, Shaniya
8.Solomon, Lacory
9. Young, Tytiana