Classification Continued


John Quinn

UNGP Makerere

Pulse Lab Kampala

AI Research group, Makerere University

Data Science Africa 2016

We make sense of the world by dividing things up into categories.

Karpathy and Fei-Fei (2014),


scientia (Latin: knowledge)

*skei- (Proto-Indo-European: to cut, to split)

Applications of classification:

  • Face detection
  • Face recognition
  • Medical imaging
  • Spam detection
  • Optical character recognition
  • Sentiment analysis
  • Biometric identification
  • Speech recognition


Diagnosis of cassava diseases

Diagnosis of banana diseases

Classification of households into food-secure and food-insecure based on attributes.

Household indicator variables are: distance to road (dr), land size (ls), age of household head (age), income (inc), distance from house to garden (dg), all expressed in percentiles of the training data.

How would you write software to classify handwritten digits?

How about just distinguishing between 1s and 2s?

Image data is just grids of numbers, and can be represented as 1D arrays:

We need a similarity, or "distance" measure...

We need a similarity, or "distance" measure...

We need a similarity, or "distance" measure...