Week 12 · 45-60 min

Review and Wrap-Up

Week Summary

A quick preview of the main ideas in this week before you begin the slides.

  • Reviewing Core AI/ML Concepts
  • Reviewing the ML Workflow
  • Reviewing Models Covered
  • Reviewing Evaluation Skills

Glossary

Review these key terms before starting this week. They are kept simple so students can learn the new vocabulary before moving into the slides.

Reviewing Core AI/ML Concepts
Reviewing Core AI/ML Concepts
Reviewing the ML Workflow
Reviewing the ML Workflow
Reviewing Models Covered
Reviewing Models Covered
Slide 1💡

Reviewing Core AI/ML Concepts

Learning goal

Consolidate all AI & ML concepts before the capstone project.

Read one card at a time. Focus on the main idea first, then look at the visual on the right to help it stick.

Review the difference between AI and machine learning.

Revisit supervised versus unsupervised learning.

Review classification versus regression.

These concepts are the foundation of the module.

Confidence here supports the capstone.

Remember

A strong conceptual foundation makes later model choices and evaluations much more meaningful.

Reviewing Core AI/ML Concepts

Reviewing Core AI/ML Concepts

Slide 1 of 8

Use arrows or dots to move through the lesson

Practice

  1. Write 2-3 sentences about what you learned this week.

Coming Next Week: Take the weekly quiz, then continue to the next week.

Project Brief

Complete the "Train and Evaluate a Simple Model" project across weeks 11-12.

What to Build

  1. Day 1: Choose or receive a small dataset and define the prediction task and question.
  2. Day 2: Clean and structure the dataset; identify features and label.
  3. Day 3: Split data into training and testing sets.
  4. Day 4: Choose and train an appropriate model such as linear regression, logistic regression, or a decision tree.
  5. Day 5: Generate predictions on the test data.
  6. Day 6: Calculate appropriate evaluation metrics such as accuracy or error and a confusion matrix if relevant.
  7. Day 7: Compare training and testing performance to look for overfitting or underfitting.
  8. Day 8: Write a short reflection on potential bias or limitations.
  9. Day 9: Prepare a short presentation summarizing the task, model, results, and limitations.
  10. Day 10: Present findings and answer questions.

Self-Assessment Rubric

  • Correct ML workflow implementation — 30%
  • Appropriate model choice and evaluation — 25%
  • Depth of ethics, bias, and limitations reflection — 20%
  • Code clarity and comments — 10%
  • Presentation clarity — 15%