#Overview

FMRI is a technology used to measure brain activity, typically while the subject is performing a task in the MRI scanner. It works by measuring how blood flow changes in response to neuronal activity. The resulting data are a time series of brain volumes. After pre-processing, we can compute fMRI brain maps which roughly reflect locations of brain activity corresponding to the task the subject was involved in i.e. a single map for each subject/activity. One important topic in neuroscience is to assess to what extent one can predict behavior from fMRI brain maps. This is known as “decoding” or “brain reading” [2, 3]. Each brain image may be associated with multiple cognitive processes (sub-tasks). Ideally, decoding can predict which sub-tasks are associated with each brain map [1] -- denoted as a set of labels. 
The prediction problem which involves predicting a set of labels is known as multilabel classification. It is often thought of as the simplest kind of structured prediction [4, 5]. 

References:

[1] Schwartz, Yannick, Bertrand Thirion, and Gaël Varoquaux. "Mapping paradigm ontologies to and from the brain." Advances in neural information processing systems. 2013.

[2] Varoquaux, Gael, and Bertrand Thirion. "How machine learning is shaping cognitive neuroimaging." GigaScience 3.1 (2014): 28.

[3] Pereira, Francisco, Tom Mitchell, and Matthew Botvinick. "Machine learning classifiers and fMRI: a tutorial overview." Neuroimage 45.1 (2009): S199-S209.

[4] Tsoumakas, Grigorios, and Ioannis Katakis. "Multi-label classification: An overview." International Journal of Data Warehousing and Mining 3.3 (2006).

[5] Koyejo, Oluwasanmi O., et al. "Consistent multilabel classification." Advances in Neural Information Processing Systems. 2015.

Your goal is to predict a set of labels associated with each fMRI brain image. You can use any technique you like. Your project will be judged based on performance on two metrics: *Subset Accuracy* and *Micro-averaged AUC*.