Machine Learning Standard

Create a machine learning prototype in 10 sessions

Secondary and FE

10-11 Sessions

In-class or extracurricular

Basic programming

Course Summary

  • Students explore a wide range of machine learning applications and assess the social, legal and ethical impact of the use of AI algorithms
  • Student work in teams or individually to design and build a prototype that solves a problem they care about using machine learning altorithms
  • Students work their way through a range of activities, split across 10-11 sessions
  • See below for the scheme of work, student workbook, and learning objectives

Course sessions

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  1. Session 1: What is machine learning?
  2. Session 2: Facial recognition

  3. Session 3: Natural Language Processing

  4. Session 4: Recommendation Systems

  5. Session 5: Decisions and Ethics

  6. Session 6: Putting It All Together

  7. Session 7: Spotting problems

  8. Session 8: Plan your model

  9. Session 9: Industry Engagement Session (Optional)

  10. Session 10: Build and Test Model

  11. Session 11: Pitch your model

  12. Session 12: Algorithms (optional)

  13. Session 13: Using Python and Orange (Optional)

  14. Session 14: Careers in Machine Learning (Optional)

Core resource

Course workbook for students

  • Printable student A4 workbook containing practical activities.
  • Guides you and your students through the course.
  • Fully editable, making it easy for you to adopt to meet your needs.

Core resource

Scheme of Work

  • Get a quick overview of the course structure
  • Review the learning objectives and otucomes for each session