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
Login or sign up now to access all of the sessions
- Session 1: What is machine learning?
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Session 2: Facial recognition
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Session 3: Natural Language Processing
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Session 4: Recommendation Systems
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Session 5: Decisions and Ethics
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Session 6: Putting It All Together
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Session 7: Spotting problems
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Session 8: Plan your model
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Session 9: Industry Engagement Session (Optional)
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Session 10: Build and Test Model
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Session 11: Pitch your model
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Session 12: Algorithms (optional)
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Session 13: Using Python and Orange (Optional)
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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.
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Session 1
What is machine learning?
Learning objectives
Core
- Understand what machine learning is
- Understand how machines learn
Challenge
- Understand how machines learn to identify images
- Understand what neural networks are
Go to session
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Session 2
Facial recognition
Learning objectives
Core
- Understand how facial recognition works
- Explore some of the uses of facial recognition
Challenge
- Understand some of the issues surrounding facial recognition
- Understand how there can be bias in facial recognition
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Session 3
Natural Language Processing
Learning objectives
Core
- Understand what a chatbot is
- Understand how to build a natural language recognition prototype
Challenge
- Recognise the advantages of natural language processing
- Appreciate real life applications of chatbots
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Session 4
Recommendation Systems
Learning objectives
Core
- Understand how machine learning can be used to make recommendations
- Understand what a filter bubble is
Challenge
- Understand the potential dangers of recommendation systems
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Session 5
Decisions and Ethics
Learning objectives
Core
- Understand how machine learning is used to make decisions
- Understand potential issues with the application of machine learning
Challenge
- Be able to evaluate the impact of ethical considerations in the application of machine learning
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Session 6
Putting It All Together
Learning objectives
Core
- Understand the potential impact of machine learning on employment and careers
- Be able to summarise your learning in a written report
Challenge
- Be able to express your views clearly
- Be able to evaluate the impact of machine learning across a range of different applications
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Session 7
Spotting problems
Learning objectives
Core
- Identify everyday problems which could be solved using machine learning
- Gain a better understanding of the data requirements of your machine learning idea
Challenge
- Be able to convey your ideas clearly and concisely
- Be able to critically evaluate machine learning ideas and select the strongest to take forward
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Session 8
Plan your model
Learning objectives
Core
- Gain a better understanding of the data requirements of your machine learning idea
- Gain a better understanding of who would use your machine learning model and who would benefit
Challenge
- Be able to prepare data for a machine learning model
- Be able to design a machine learning model that will meet the needs of the user
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Session 9
Industry Engagement Session (Optional)
Learning objectives
Core
- Gain information about the machine learning model development process from an industry Expert
- Be able to obtain constructive feedback on your machine learning ideas
Challenge
- Develop confidence discussing technical feasibility with an industry Expert
- Be able to act on constructive feedback to improve your machine learning model
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Session 10
Build and Test Model
Learning objectives
Core
- Be able to develop and train your machine learning model
Challenge
- Be able to improve the accuracy of your model
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Session 11
Pitch your model
Learning objectives
Core
- Understand how to present your ideas effectively
Challenge
- Be able to summarise the key points of your machine learning model in a short presentation
- Understand different business models used for machine learning
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Session 12
Algorithms (optional)
Learning objectives
Core
- Understand the difference between supervised and unsupervised learning
- Understand basic machine learning algorithms
Challenge
- Be able to use basic machine learning algorithms
- Understand what a neural network is
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Session 13
Using Python and Orange (Optional)
Learning objectives
Core
- Be able to use machine learning tools to visualise data
Challenge
- Be able to use python to import data and run basic machine learning algorithms
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Session 14
Careers in Machine Learning (Optional)
Learning Objectives
Core
- Understand the range of jobs available developing machine learning
Challenge
- Understand the different routes into a career in machine learning