AdaptiveDecisionMaking_2018 (ADM)

Repository for code and lab resources for "Neural and Cognitive Models of Adaptive Decision Making" course (2018)

Jupyter notebooks

Click on binder badge above to run jupyter notebooks for labs and homework. Or download the ipynb files here to run locally.

Instructions for getting started

Install Anaconda with Python 3.6:

• OSX
• Linux
• Windows

Confirm installs

# check that your system is now using Anaconda's python
which python

# and that you installed Python 3.6
python -V

Install ADMCode package

ADMCode is a python package with custom code that can be used to complete the labs and homeworks (which will both be in the form of jupyter notebooks)

pip install --upgrade ADMCode

Working with git

Git is full of weird nonsense terminology. This tutorial is a super useful resource for understanding how to use it.

• If you don't already have a github account, create one here
• Install git command-line tools (see setup section here)

Clone ADMCode

• Open a terminal and cd to a directory where you want to download the ADMCode repo (example: cd ~/Dropbox/Git/)
• Next, use git to clone the remote ADMCode repository to create a local repo on your machine

# make sure you've done steps 1 and 2
# before executing this in your terminal
git clone https://github.com/CoAxLab/AdaptiveDecisionMaking_2018.git

Pull updates

• Use git pull to update your local repo with any changes to the remote ADMCode repo
• In the command below, origin is the default name pointing to the remote repo on Github
• master is the branch of the remote that you want to sync with

# first cd into your local ADMCode repo
# (same directory as step 1 in "Clone ADMCode" ^^^)
git pull origin master

Useful resources

• Anaconda distribution: package management for scientific python (& R)
• Jupyter: interactive python interpreter in your browser (tutorial)
• pandas: tabular dataframe manager (tutorial)
• numpy: numerical computing library (tutorial)
• scikit-learn: data science and machine learning library (tutorial)
• matplotlib: plotting and visualization library (tutorial)
• seaborn: wrapper for making matplotlib pretty, plays nice w/ pandas (tutorial)
• and more...