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 10-15 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

Core resource

Expert Feedback Tool

This video explains how to use our brand new Expert Feedback tool to obtain personalised feedback on your project from our Industry Expert volunteers.