Machine Learning Meetup Notes: 2010-05-26

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  • Andy gave overview of where we're at with KDD data
  • Mike S gave presentation:
    • Gaussian Mixture Models
    • k-means clustering
    • very basic expectation-maximization
  • Brainstorming session on how to reduce skill set column
    • Tom tried to quantify opportunity per skills per row as high dimensional vector
  • Brainstorming on how to reduce other data and compute new features
    • Tom assigned to k-means clustering of skills
    • Andy assigned to computing new features: step/problem = student IQ, step complexity
    • Mike assigned to trying to use self-organizing maps to reduce skills
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