Machine Learning Meetup Notes: 2010-05-26

• 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