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 for the KDD Dataset
- Tom will apply k-means clustering of skills (or steps), for data reduction
- Andy will compute new features: unique step/problem id, student IQ (avg. correct), step challenge/difficulty (avg correct), step complexity (# skills required)
- Mike will use self-organizing maps to reduce skills
- Paul will visualize/summarize the data, to provide understanding and insight
- Mike will set up an FTP server for people to transfer their enormous datasets
- Theo will use some Weka classifiers to produce a classification method for the data