Machine Learning Meetup Notes: 2010-05-26 - Revision history

ThomasLotze at 05:09, 27 May 2010

2010-05-27T05:09:26Z

← Older revision		Revision as of 22:09, 26 May 2010
Line 6:		Line 6:
	*Brainstorming session on how to reduce skill set column		*Brainstorming session on how to reduce skill set column
	**Tom tried to quantify opportunity per skills per row as high dimensional vector		**Tom tried to quantify opportunity per skills per row as high dimensional vector
	*Brainstorming on how to reduce other data and compute new features		*Brainstorming on how to reduce other data and compute new features for the KDD Dataset
	**Tom ~~assigned to~~ k-means clustering of skills		**Tom will apply k-means clustering of skills (or steps), for data reduction
	**Andy ~~assigned to computing~~ new features: step/problem = student IQ, step complexity		**Andy will compute new features: unique step/problem id, student IQ (avg. correct), step challenge/difficulty (avg correct), step complexity (# skills required)
	**Mike ~~assigned to trying to~~ use self-organizing maps to reduce skills		**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

SpammerHellDontDelete at 04:47, 27 May 2010

2010-05-27T04:47:52Z

← Older revision		Revision as of 21:47, 26 May 2010
Line 9:		Line 9:
	**Tom assigned to k-means clustering of skills		**Tom assigned to k-means clustering of skills
	**Andy assigned to computing new features: step/problem = student IQ, step complexity		**Andy assigned to computing new features: step/problem = student IQ, step complexity
	**		**Mike assigned to trying to use self-organizing maps to reduce skills

SpammerHellDontDelete at 04:45, 27 May 2010

2010-05-27T04:45:12Z

← Older revision		Revision as of 21:45, 26 May 2010
Line 8:		Line 8:
	*Brainstorming on how to reduce other data and compute new features		*Brainstorming on how to reduce other data and compute new features
	**Tom assigned to k-means clustering of skills		**Tom assigned to k-means clustering of skills
			**Andy assigned to computing new features: step/problem = student IQ, step complexity
			**

SpammerHellDontDelete: Created page with '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…'

2010-05-27T04:44:12Z

Created page with '*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…'

New page

*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

Machine Learning Meetup Notes: 2010-05-26 - Revision history

ThomasLotze at 05:09, 27 May 2010

SpammerHellDontDelete at 04:47, 27 May 2010

SpammerHellDontDelete at 04:45, 27 May 2010

SpammerHellDontDelete: Created page with '*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…'

SpammerHellDontDelete: Created page with '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…'