Nice summary; I like the format as well. However, the title of the cheat sheet is misleading since (a) many of the algorithms listed can be used for non-linear classification and (b) some of them can be considered supervised learning, such as naive Bayes and perceptron since they're trained with sample inputs and expected outputs (supervisory signals).
Otherwise, this is awesome. Hopefully you will add to it, and make it available in web form.
I've changed the title to "Algorithms for Supervised- and Unsupervised Learning", which is definitely more appropriate. Initially the cheat sheet only contained linear classifiers, hence the misleading title.
Comments
Nice summary; I like the format as well. However, the title of the cheat sheet is misleading since (a) many of the algorithms listed can be used for non-linear classification and (b) some of them can be considered supervised learning, such as naive Bayes and perceptron since they're trained with sample inputs and expected outputs (supervisory signals).
Otherwise, this is awesome. Hopefully you will add to it, and make it available in web form.
Thanks for the feedback!
I've changed the title to "Algorithms for Supervised- and Unsupervised Learning", which is definitely more appropriate. Initially the cheat sheet only contained linear classifiers, hence the misleading title.