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

Course sessions

Login or sign up now to access all of the sessions

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