| Goals: |
Introduce and study the philosophy, utility, and models of
connectionist computing, such that students are able to
propose original research with potential follow-up in a
graduate research program. Expand the creativity of the
students in all aspects of computing.
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| Text: |
Prepared packet of overhead copies which can be purchased at the
bookstore. You will be expected to read the assigned
literature before and optionally after the scheduled
lecture. To help motivate reading I will pass around a
sheet for you to mark whether you have done a complete
and careful reading, a partial reading, or no reading.
A total reading counts for 3 points, partial - 1 point,
none - 0 points. Each day mark if you have done the
reading for the lecture to be given that day. The
grading will be non-linear such that missing one or
two readings does not hurt much, but it picks up fast
after that.
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| Prerequisites: |
Senior or Graduate standing, 380, 312, Math 343 (linear algebra), Creativity.
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| Software: |
You can do your simulations, etc. in the department
open labs using the NeuroSolutions simulator which
comes with the book. It is already installed on the
open lab machines so you should not need the CD for
that. It looks like a pretty good simulator with
numerous options. The version we have does not allow
us to save weights however.
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| Extra Literature: |
I have a number of papers in my office that can be
looked over and copied under constraint of the 20
minute rule (The paper must be back to me within 20
minutes). I can also send for most any paper you wish
through interlibrary loan, (and will do so), but it
usually takes 2-3 weeks, so plan ahead.
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| Grading (~): |
Reading: 10%, Simulations and Homework: 25%, Midterm:
21%, Project: 22%, Final: 22% (Fri., Dec. 15,
7am-10am). Grading is on a curve and some amount of
subjectivity is allowed for attendance, participation,
perceived effort, etc. If you think, you'll be all right.
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| Late assignments: |
Assignments are expected on time (beginning of class on
due date). Late papers will be marked off at 5%/school
day late. However, if you have any unusual
circumstances (sick, out of town, unique from what all
the other students have, etc.), which you inform me of,
then I will not take off any late points. Nothing will
be accepted after the last day of class instruction.
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| Project: |
An in-depth effort on a particular aspect of neural
networks. A relatively extensive literature search in
the area is expected with a subsequent bibliography.
Good projects are typically as follows: Best: Some of
your own original thinking and proposal of a network,
learning paradigm, system, etc. This (and other
projects) are typically well benefited by some computer
simulation to bear out potential. Very Good: Starting
from an in-depth study of some current model, strive to
extend it through some new mechanisms. Not Bad: A
study of a current model with an in-depth analysis of
its strengths, weaknesses, potential, and suggested
research. Not Good: A description of a current model.
The earlier you start the better. Note that in a
semester course like this, you will have to choose a
topic when we have only covered half of the material.
That does not mean your project must cover items
related to the first half of the semester. You should
use your own initiative and the resources available
(library literature, texts, me, etc.) to peruse and
find any topic of interest to you, regardless of
whether we have or will cover it in class. Interesting
models which we will probably not have time to cover
in-depth in class include: Feldman nets, Kohonen maps,
HOTLU's, BAMs, CMAC, ASN, Cognitron, Neo-Cognitron,
BolzCONS, Michie Boxes, Cauchy Machines,
Counterpropagation, Madaline II, Associative Networks,
RCE, etc.
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