Welcome!

This is the blog for the Cognitive Science Seminar (COMP/PHIL/PSYC 7514/8514) at the University of Memphis, fall 2018.

First things first (press READ MORE to see the whole post):

1. See the syllabus
2. Sign up for a day to present on the Doodle poll
3. Subscribe for email updates
Then each week, you should:

1. Access weekly readings
2. Post your responses to readings using the "Post a Comment" button
3. Prepare a presentation if it's your week

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Popular posts from this blog

Readings for 9/12/18

Hello again 😃 Please make sure you've subscribed to email updates as described in the  welcome post . As before, I've put up the readings for next week (see tab above). You will need your UoM email id/password to log in (e.g. foobar@memphis.edu would log in as foobar). Make sure your respond with your comments by 9/11 at noon. Make your comments by replying/commenting to this post (the one you are looking at now). If your post from last week showed up at "Unknown," this is because your profile settings are not public. Click here to change your settings.
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Readings for 9/26/18

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Readings for next week are now posted. Although the readings are not ordered, the  order listed is probably the best one.  Make sure your respond with your comments by 9/25 at noon. A few " goodies " follow: a humorous cartoon about curve fitting, and an example of boosting. The following example of boosting is taken from here . I've removed the code and some of the math below to make it easier to follow. 1. Start with some data. We use the normal distribution. 2. Fit an initial model. Here we fit a line. You could view this as an intercept-only line or a line with a slope of zero. In either case, it's not hard to see that the residuals are balanced above and below the line, which is what line fitting is supposed to do. 3. Fit a model on the residuals from step 2 (step 2 residuals). This time we use a decision stump , which is a decision tree with one branch point. This is the stump we fit: As you can see, any x (the horizontal axis) less than
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