A pathway from foundations to application
This self-directed pathway organises artificial intelligence into four connected stages. It is a subject exploration resource rather than an accredited programme specification. Work through the stages at a pace that allows time for reading, practice and reflection.
1. Linear algebra
Focus on linear algebra as a way to understand models that learn from data and the limits of automated decisions. Build a concise glossary, identify the central models and explain one idea without relying on specialist vocabulary.
Learning activity: select a published example related to linear algebra. Identify the question, evidence and conclusion. Reconstruct the reasoning and note one assumption that could change the result.
Portfolio evidence: write a short critical explanation of how linear algebra connects with machine learning. Include an annotated diagram, worked example or source comparison where appropriate.
2. Machine learning
Focus on machine learning as a way to understand models that learn from data and the limits of automated decisions. Build a concise glossary, identify the central models and explain one idea without relying on specialist vocabulary.
Learning activity: select a published example related to machine learning. Identify the question, evidence and conclusion. Reconstruct the reasoning and note one assumption that could change the result.
Portfolio evidence: write a short critical explanation of how machine learning connects with natural language processing. Include an annotated diagram, worked example or source comparison where appropriate.
3. Natural language processing
Focus on natural language processing as a way to understand models that learn from data and the limits of automated decisions. Build a concise glossary, identify the central models and explain one idea without relying on specialist vocabulary.
Learning activity: select a published example related to natural language processing. Identify the question, evidence and conclusion. Reconstruct the reasoning and note one assumption that could change the result.
Portfolio evidence: write a short critical explanation of how natural language processing connects with responsible ai. Include an annotated diagram, worked example or source comparison where appropriate.
4. Responsible AI
Focus on responsible ai as a way to understand models that learn from data and the limits of automated decisions. Build a concise glossary, identify the central models and explain one idea without relying on specialist vocabulary.
Learning activity: select a published example related to responsible ai. Identify the question, evidence and conclusion. Reconstruct the reasoning and note one assumption that could change the result.
Portfolio evidence: write a short critical explanation of how responsible ai connects with linear algebra. Include an annotated diagram, worked example or source comparison where appropriate.