Thanks for making it to the end of this Jupyter Book! I hope you found these course materials helpful! Although the covered topics are not exhaustive or comprehensive, I encourage checking out this growing list of literature that extends beyond the purview of the course.
Contributions from the community to extend the breath and depth of tutorials are most welcome! These extensions may include improving or expanding existing content (e.g., stylistic improvements, alternative optimization algorithms), adding new tutorials on topics covered throughout the course, or adding new tutorials on related topics not covered throughout the course. Here are some tutorials on computational models of human social behavior and neuroscience that I hope to add in the future! These topics were covered in the original course via lectures, readings, and discussions, but not implemented in the Jupyter Book.
Model-based versus model-free learning in social contexts
Learning about the states/beliefs of others
Interactive Partially Observable Markov Decision Processes (iPOMDPs)
Integrating personal and social information (e.g., social evidence accumulation)
All topics with functional magnetic resonance imaging (fMRI) data
Please submit any feedback on errors, omitted credit, or new ideas for this evolving project, which is licensed under Creative Commons Attribution Share Alike 4.0 International. These can be communicated by opening an issue on GitHub. All contributions will be credited and should follow the community guidelines outlined on the Contribution page.