Learning Machines: w2 notes


huffman encoding: scan entire doc, look at frequency of occurrence in overall document, then encode based on frequencies

  • scalar: individual number
  • vector: one dimensional list of numbers
  • matrix: a list of vectors, two dimensional list of numbers
  • tensor: a list of matrices is one example of a tensor

k-means clustering (viz demonstration here)

  1. choose random points to serve as center of cluster
  2. measure distances between “center” points and data points
  3. sort points into clusters based on distances
  4. move center points to actual center of clusters
  5. measure new distances between “center” points and data points (newly adjusted points may change which points belong to which cluster)
  6. repeat until stable (points no longer jump between clusters)

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