Machine Learning Standard

Create a machine learning prototype in 10 sessions

  • 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

Secondary and FE

10-11 Sessions

In-class or extracurricular

Basic programming

Machine Learning Standard 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.

Scheme of Work

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

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.

Course sessions

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Session 1: What is machine learning?

Objective: To understand what machine learning is

Session 2: Facial recognition

Objective: To understand how facial recognition works

Session 3: Natural Language Processing

Objective: To understand what a chatbot is

Session 4: Recommendation Systems

Objective: To understand how machine learning can be used to make recommendations

Session 5: Decisions and Ethics

Objective: To understand how machine learning is used to make decisions

Session 6: Putting It All Together

Objective: To understand the potential impact of machine learning on employment and careers

Session 7: Spotting problems

Objective: To understand how to identify everyday problems which could be solved using machine learning

Session 8: Plan your model

Objective: To gain a better understanding of the data requirements of your machine learning idea

Session 9: Industry Engagement Session (Optional)

Objective: To gain information about the machine learning model development process from an industry Expert

Session 10: Build and Test Model

Objective: To be able to develop and train your machine learning model

Session 11: Pitch your model

Objective: To understand how to present your ideas effectively

Session 12: Algorithms (optional)

Objective: To understand the difference between supervised and unsupervised learning

Session 13: Using Python and Orange (Optional)

Objective: To be able to use machine learning tools to visualise data

Session 14: Careers in Machine Learning (Optional)

Objective: To understand the range of jobs available developing machine learning