Models of Memory

Welcome! This repository contains course materials for the Dartmouth graduate course on computational models of learning and memory. The syllabus may be found here. Feel free to follow along with the course materials (whether you are officially enrolled in the course or just visiting!), submit comments and suggestions, etc.

A note about this Open Course

This course is taught as an Open Course, meaning that the course is designed from the ground up to be shareable and freely accessible to anyone. All code for this course is written in Python and most of the material is organized as either Jupyter notebooks or Markdown files.

Feel free to follow along with this course, do the assignments, post questions and/or issues to this repository or Discord, suggest changes, etc. However, I won’t formally evaluate your submitted work unless you are a Dartmouth student who is currently enrolled.

If you are a course instructor teaching overlapping material, feel free to borrow any materials used in this course! If you directly copy (or “draw heavy inspiration from”) the materials, I would appreciate a citation (e.g., a pointer to this repository). I’d also love to hear from you about how you’re using this resource!

This course is a continually evolving work in progress. I plan to update the material to keep the syllabus fresh and relevant. By the same token, although my goal is 100% accuracy and currency, it’s unlikely that I’ll achieve that goal. You should participate with the understanding that this material will likely have occasional mistakes, omissions, errors, etc. Given this fact, one way to approach the course is to maintain an open yet critical view of the material. If you think there’s a mistake, I encourage you to bring it to my attention!