Explore Artificial Intelligence
Artificial Intelligence examines models that learn from data and the limits of automated decisions. This subject guide introduces the questions, methods and practical work that help learners understand the field before choosing a formal course of study.
A strong starting point is to connect linear algebra with machine learning. The first develops a foundation for reasoning; the second provides a setting in which to test ideas. Keep a learning journal that records assumptions, sources and questions that remain unresolved.
Four areas to investigate
Linear algebra
Connect linear algebra to the study of models that learn from data and the limits of automated decisions. Compare an introductory explanation with a worked example, then describe where the approach is useful and where it may fail.
Machine learning
Connect machine learning to the study of models that learn from data and the limits of automated decisions. Compare an introductory explanation with a worked example, then describe where the approach is useful and where it may fail.
Natural language processing
Connect natural language processing to the study of models that learn from data and the limits of automated decisions. Compare an introductory explanation with a worked example, then describe where the approach is useful and where it may fail.
Responsible AI
Connect responsible ai to the study of models that learn from data and the limits of automated decisions. Compare an introductory explanation with a worked example, then describe where the approach is useful and where it may fail.
Learn through inquiry
Compare two text-classification models using an explicit error analysis and a documented dataset.
Begin with a question narrow enough to investigate. Decide what evidence would support an answer, document the method and explain what further work would be needed.