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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