AI curriculum
Course outline for Artificial Intelligence Foundations
AI terminology can make solution choices difficult. Develop a practical model of how AI systems learn and generate outputs, then assess their suitability for a task.
About Artificial Intelligence Foundations
AI terminology can make solution choices difficult. Develop a practical model of how AI systems learn and generate outputs, then assess their suitability for a task.
Artificial Intelligence Foundations Course Objectives
- Distinguish machine learning, deep learning, and generative AI.
- Explain training and inference with examples.
- Identify capability limits in an AI use case.
Pre-requisites
- Basic computer literacy.
- Interest in evaluating AI applications; coding is not required.
Lab Setup
- Computer with a browser and a text editor.
- Use supplied model output examples or an optional locally available model; no paid service is required.
Detailed Course Outline
Proposed modulesModule 1: Learning and generation
- Machine learning foundations
- Neural networks
- Deep learning
- Generative AI
Practical outcome: Classify example tasks by the kind of AI they need.
Module 2: Using models responsibly
- Foundation models
- Training vs inference
- Model capabilities and limitations
Practical outcome: Document assumptions and limitations for a model-assisted task.
Practical exercise
- Assess an AI use case and propose checks for incorrect or unsuitable outputs.
How we train
Contact us for full course details, including duration, delivery options and lab requirements.
Ask about exercises, instructor feedback and the prior knowledge you need. Tool-specific courses marked provisional may become modules in a broader course.
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