Module 2 - Lesson 6: Emergent systems, strange loops, and supervised and unsupervised learning techniques

[turukawa]

ETHICS

Consider the implications of strange loops – self-referential programs with emergent properties – on analysis.

Douglas Hofstadter developed the concept of strange loops; self-referential and paradoxical systems. DNA acts upon itself, and machine intelligence systems can adjust their own programs. When a system enters a strange loop, who is responsible for the outcomes of its decisions? Example: machine intelligence systems where we don’t know how they make decisions.

CURATION

Analyse how data and methodology are curated from emergent systems.

Getting AI to explain their methodology and document how they do things; Bias in machine intelligence, from not recognising female voices, or racial bias, is a result of a chain of events. How curate these properties?

ANALYSIS

Categorise data using k-nearest neighbours for unsupervised learning clustering of data.

K-nearest neighbours, systems for training and testing; Supervised vs unsupervised learning; k-means clustering vs k-nearest

PRESENTATION

Plot supervised and unsupervised learning outcomes using decision boundaries.

Dendrograms and decision boundaries using meshgrid.

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CASE STUDY

Chronic Kidney Disease? Continue leukaemia, or look for something new on Figshare or Dataverse?