CogSci Seminar Fall 2018

Apologies for the late post! Please put your comments for this week's reading below.

These are up (as off last week). Please respond to this post with your comments :)

There is no class 11/21 because of the Thanksgiving holiday, see the University calendar . Feel free to reach out if you have any questions/concerns about your paper/project. I will be in town and on email :)

Readings are up! We have an external speaker next week, Burt Monroe. I distributed a Doodle poll for people to sign up to meet with him; please let me know if you didn't get it (which means you're not on the IIS mailing list).

Readings are up for next week 😀 In addition to the speaker's reading I've assigned some background reading on SVD and latent semantic analysis/indexing. These are closely related to PCA and are a kind of unsupervised learning.

Readings are up for next week 😀 Please also take a look at the data description for the latest Kaggle lab (link distributed yesterday). The lab for next week will step through the Zuur protocol to build a predictive model for this dataset.

Readings are up in the normal place. This week's readings are fairly deep into neural networks, which we haven't really talked about. Rather than give you something else to read, I'm going to try to give you what you need to know in this post. First, there are two kinds of neural networks, those that have recurrent connections and those that don't. A recurrent connection is something like this: The circle represents a neuron (AKA node; unit) and the arrow represents a connection from the neuron back to itself. Recurrent connections can also be more distant, e.g. from a child neuron to its parent, as long as there is a path that leads back to the originating neuron, e.g. child --> parent --> child. The important thing to understand about recurrent neural networks is that they are dynamical systems that unfold in time. Imagine that we initialize the neuron above to have a value (AKA energy; activation) of 1 and the recurrent connection to have a streng...

Readings are up for next week :) Following up on class discussion yesterday, here is the paper that I thought of assigning but was concerned about overloading people: https://www.microsoft.com/en-us/research/wp-content/uploads/2016/05/Bishop-MBML-2012.pdf This is not assigned, but it's an excellent paper and feedback is welcome. Also, someone asked about Gaussian processes yesterday. In thinking about the whole context of that discussion, I thought it might be helpful to recap some terminology. A random process is something that creates non-deterministic (uncertain) outcome Examples: Rolling dice (fair dice or not) Flipping a coin (fair coin or not) Random sampling  Counterexamples Flipping a coin with two heads (because there is only 1 outcome, it is deterministic/certain) A random variable maps the outcome of a random process  to a number. Example: Flipping a coin Random process: flipping a coin Random variable X where 1 is heads and 0 is tail...

Readings are now up at the regular location. They are a bit shorter this week but more dense 😸 Please post by 10/9 at noon. Given our discussion last week, I thought I'd share two stories (one recent) about bad research practices and ethics: https://www.npr.org/sections/thesalt/2018/09/26/651849441/cornell-food-researchers-downfall-raises-larger-questions-for-science https://fivethirtyeight.com/features/science-isnt-broken/ Finally, a few of you have already approached me about your project. For those of you who are still thinking about it or looking for data, please see the links here: http://www.memphis.edu/iis/resources/index.php

Just put the readings up for next week; please post your comments by 10/2/18 at noon. During the lab portion we'll keep working on the exoplanet data :)

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...

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). One of the readings is what's called a "scrolly." As you scroll down, it will run animations that accompany the text. You can also scroll up to reverse the animations. Scrolling up and down might be particularly helpful (like instant replay). Make sure your respond with your comments by 9/18 at noon. Make your comments by replying/commenting to this post (the one you are looking at now).

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.

Greetings 😃 If you haven't already gone through the welcome post , please do so now. 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). I've also created a refresher video for the Kaggle kernel lab we did yesterday. We will keep working with that kernel in our lab next week:

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