Small Group Subproblems

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**Generate predictive system for how much someone should climb based on how they're doing that week.
 
**Generate predictive system for how much someone should climb based on how they're doing that week.
 
**Noted that amount of deep sleep can be correlated to mood, which can be correlated to climbing.
 
**Noted that amount of deep sleep can be correlated to mood, which can be correlated to climbing.
**Suggestion: we all track one aspect and combine it into one big dataset.
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**Suggestion: we all track one aspect of our daily routines and combine it into one big dataset.
 
*4square Checkins (Joe H)
 
*4square Checkins (Joe H)
 
**Use 4square checkins to find out what would be the best time to visit a place in a region.
 
**Use 4square checkins to find out what would be the best time to visit a place in a region.
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*Kinects (Jared, Praveen)
 
*Kinects (Jared, Praveen)
 
**Get another kinect and do awesome shit with it.
 
**Get another kinect and do awesome shit with it.
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*Music Recommendation System (Jared)
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**Given different people in a space (who voluntarily "checkin"), and a jukebox, play music that appeals to their tastes.

Revision as of 21:40, 16 February 2011

Joe H proposed that we come up with some smaller problems that allow people to interactively solve them in short periods of time in ML meetups. We can construct a git hub project for them, create a skeleton project, and use issue tracking to list the problems and work on them. Here we list these problems and plan to build them out.

  • Time Series Data (Erin)
  • Rock Climbing (Joe H)
    • Rock climbing data analysis
    • Generate predictive system for how much someone should climb based on how they're doing that week.
    • Noted that amount of deep sleep can be correlated to mood, which can be correlated to climbing.
    • Suggestion: we all track one aspect of our daily routines and combine it into one big dataset.
  • 4square Checkins (Joe H)
    • Use 4square checkins to find out what would be the best time to visit a place in a region.
  • Education Learning Problem (Tom)
    • Determine how people learn over time.
    • Figure out how difficult questions are, what kind of knowledge they test
    • Determine how good are people at certain types of knowledge.
  • Auditory Brain Cells (Mike S)
    • Soon to be posted data on crcns.org of auditory brain cells.
    • Figure out how to predict brain cell output.
  • Deep Belief Networks for Images/Sounds (Mike S)
    • Train deep belief networks on images, project output to monitor to see what network is "thinking"
    • Do same for sounds
    • Use auto-encoders to recall memories based on new input
  • Kinects (Jared, Praveen)
    • Get another kinect and do awesome shit with it.
  • Music Recommendation System (Jared)
    • Given different people in a space (who voluntarily "checkin"), and a jukebox, play music that appeals to their tastes.
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