The first week of the course is designed to introduce the course, and to provide a basic introduction to some of the general characteristics of connectionist models. We then jump into the theory of connectionism by considering a first building block for artificial neural networks: associations. We describe the laws of association, focusing upon association via contiguity. We then consider a basic network -- a distributed memory, or the standard pattern associator -- as well as two rules for modifying connection weights to impose associations between input and output processors

After the lecture, our in-class activity will involve getting started with the James software package. The link to the software, exercises, etc. is provided on the right at the top; a description of the activity is provided on the right at the bottom.