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
Login or sign up now to access all of the sessions
- Session 1: What is machine learning?
-
Session 2: Facial recognition
-
Session 3: Natural Language Processing
-
Session 4: Recommendation Systems
-
Session 5: Decisions and Ethics
-
Session 6: Putting It All Together
-
Session 7: Spotting problems
-
Session 8: Plan your model
-
Session 9: Industry Engagement Session (Optional)
-
Session 10: Build and Test Model
-
Session 11: Pitch your model
-
Session 12: Algorithms (optional)
-
Session 13: Using Python and Orange (Optional)
-
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
-
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
-
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
-
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
-
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
-
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
-
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
-
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
-
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
-
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
-
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
-
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
-
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
-
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
-
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