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