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SAAII The Security and AI Institute

Data and Machine Learning Primer

Categories: Foundations of AI
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About Course

Data and Machine Learning Primer goes one level deeper than Foundations of AI for Non-Technical Professionals — for professionals who need to work with data science teams in substantive detail, review model documentation critically, and speak the language of practitioners. Still no expectation of coding, but with more time spent on the data preparation, modelling, evaluation, and deployment lifecycle in a way that lets you engage with the specifics.

By the end you will understand the data lifecycle from collection through preparation; the main model families and when they are used; the evaluation methodology and its pitfalls; the deployment considerations that affect model performance in production; and the monitoring architecture that keeps models operating well — modules that capable professional governance depends on.

Structure

  • 7 modules
  • 28 CPD hours across 6-7 weeks
  • One summative assessment — a critical review of a model documentation package
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What Will You Learn?

  • Understand the data lifecycle from collection through preparation.
  • Know the main model families and when each is used.
  • Apply evaluation methodology and recognise its pitfalls.
  • Understand deployment considerations affecting production performance.
  • Engage substantively with model documentation and validation.
  • Design monitoring that keeps models operating well.

Course Content

Module 1. The data lifecycle

  • Lesson 1.1: From raw data to training set
  • Module 1 Check

Module 2. Model families

Module 3. Evaluation methodology

Module 4. Fairness and bias in models

Module 5. Deployment considerations

Module 6. Monitoring in production

Module 7. Reading model documentation

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