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Suggestion

If you are starting with ML , I recommand you check out the Nilearn datasets. It is very friendly and might give you some ideas on what question you could explore.

Questions about ML classifiers

Hi @dllussier!
My team and I have come up with a variety of classifier models from this article and I was wondering if you had some suggestions about which ones would be good ones to start with:

NON-LINEAR

  • Nearest Neighbors (K-NN) (Cover and Hart, 1967) with K=1 and Euclidean distance metric
  • Gaussian Naïve Bayes (GNB)
  • Random Forests Classifier (RF) (Breiman, 2001) @anproulx
  • Decision trees

LINEAR (sparse l_1 regularization)

  • Support Vector Classification (SVC)
  • Logistic Regression (Hastie et al., 2009)

NON-SPARSE LINEAR (l_2 regularization)

  • Ridge classification
  • SVC
  • Logistic regression

We are thinking of starting with supervised learning and then perhaps branching out to unsupervised learning if we have time.

Type of data

I really liked the readme and the pic!
I wanted to ask you if you prefer to work on fMRI or EEG data?

adding a .gitignore file

Hey, great repo. I noticed that you have a .DS file in your repository. Most likely that came over when committed all files in the repository and then made a push from a Mac computer. If you want to keep files like these out of you repository, you can use a .gitignore file that you add to the top of your github repository. This can also be helpful when you use a text editor or IDE that creates local configuration files that you don't want to upload to your repository.

Learning goals

Hey, your README could still use some more information. How about starting with writing down what you want to learn? What tools do you want to become familiar with? That could help us suggesting what resources to use, what data sets might be suitable etc. :)

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