{"action":"created","issue":{"url":"https://api.github.com/repos/JuliaStats/Roadmap.jl/issues/11","labels_url":"https://api.github.com/repos/JuliaStats/Roadmap.jl/issues/11/labels{/name}","comments_url":"https://api.github.com/repos/JuliaStats/Roadmap.jl/issues/11/comments","events_url":"https://api.github.com/repos/JuliaStats/Roadmap.jl/issues/11/events","html_url":"https://github.com/JuliaStats/Roadmap.jl/issues/11","id":27201922,"number":11,"title":"Machine Learning Roadmap","user":{"login":"lindahua","id":1900250,"avatar_url":"https://avatars.githubusercontent.com/u/1900250?v=3","gravatar_id":"","url":"https://api.github.com/users/lindahua","html_url":"https://github.com/lindahua","followers_url":"https://api.github.com/users/lindahua/followers","following_url":"https://api.github.com/users/lindahua/following{/other_user}","gists_url":"https://api.github.com/users/lindahua/gists{/gist_id}","starred_url":"https://api.github.com/users/lindahua/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/lindahua/subscriptions","organizations_url":"https://api.github.com/users/lindahua/orgs","repos_url":"https://api.github.com/users/lindahua/repos","events_url":"https://api.github.com/users/lindahua/events{/privacy}","received_events_url":"https://api.github.com/users/lindahua/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"milestone":null,"comments":119,"created_at":"2014-02-08T15:11:07Z","updated_at":"2015-10-10T22:05:41Z","closed_at":null,"body":"Currently, the development of machine learning tools are in several different packages without little coordination. Consequently, some efforts are repetitive, while some important aspects remain lacking. \r\n\r\nHopefully, we may coordinate our efforts through this issue. Below, I try to outline a tentative roadmap:\r\n\r\n* Generalized Linear Models\r\n  - Linear Regression\r\n  - Logistic Regression\r\n  - Lasso, Elastic Net, and its variants \r\n  - Stochastic Gradient Descent\r\n\r\n  Current efforts: [GLMNet](https://github.com/simonster/GLMNet.jl), [GLM](https://github.com/JuliaStats/GLM.jl), [Regression](https://github.com/lindahua/Regression.jl)\r\n\r\n* Support Vector Machines\r\n   \r\n   Current efforts: [SVM](https://github.com/johnmyleswhite/SVM.jl), [LIBSVM](https://github.com/simonster/LIBSVM.jl)\r\n\r\n* DimensionalityReduction\r\n  - PCA\r\n  - ICA\r\n  - CCA\r\n  - Linear Discriminant Analysis\r\n  - Kernel-based methods\r\n  \r\n  Current efforts: [DimensionalityReduction](https://github.com/johnmyleswhite/DimensionalityReduction.jl)\r\n\r\n* Non-negative Matrix Factorization\r\n\r\n  This may be categorized into dimensionality reduction. However, NNMF in itself has a plethora of methodologies, and thus deserves a separate package.\r\n\r\n* Classification\r\n \r\n  There are many techniques for classification. It may be useful to have multiple packages respective techniques (e.g. GLM, SVM, kNN), and have a meta-package *Classification.jl* to incorporate them all.\r\n  \r\n* Clustering\r\n\r\n  Current efforts: [Clustering.jl](https://github.com/johnmyleswhite/Clustering.jl)\r\n\r\n* Many machine learning applications also require some supporting functionalities, such as performance evaluation, data preprocessing, etc. These can all go into [MLBase](https://github.com/lindahua/MLBase.jl)\r\n\r\n* Probabilistic Modeling (e.g. Bayesian Network, Markov Random Field, etc)\r\n\r\n  This is a huge field in itself, and may be discussed separately.\r\n\r\ncc: @johnmyleswhite @dmbates @simonster @ViralBShah\r\n\r\nEdit\r\n\r\n------------------------\r\n\r\nI created an [NMF.jl](https://github.com/lindahua/NMF.jl) package, which is dedicated to non-negative matrix factorization.\r\n\r\nAlso, a detailed plan for DimensionalityReduction is outlined [here](https://github.com/JuliaStats/DimensionalityReduction.jl/issues/8).\r\n\r\n"},"comment":{"url":"https://api.github.com/repos/JuliaStats/Roadmap.jl/issues/comments/147129262","html_url":"https://github.com/JuliaStats/Roadmap.jl/issues/11#issuecomment-147129262","issue_url":"https://api.github.com/repos/JuliaStats/Roadmap.jl/issues/11","id":147129262,"user":{"login":"dfdx","id":428171,"avatar_url":"https://avatars.githubusercontent.com/u/428171?v=3","gravatar_id":"","url":"https://api.github.com/users/dfdx","html_url":"https://github.com/dfdx","followers_url":"https://api.github.com/users/dfdx/followers","following_url":"https://api.github.com/users/dfdx/following{/other_user}","gists_url":"https://api.github.com/users/dfdx/gists{/gist_id}","starred_url":"https://api.github.com/users/dfdx/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/dfdx/subscriptions","organizations_url":"https://api.github.com/users/dfdx/orgs","repos_url":"https://api.github.com/users/dfdx/repos","events_url":"https://api.github.com/users/dfdx/events{/privacy}","received_events_url":"https://api.github.com/users/dfdx/received_events","type":"User","site_admin":false},"created_at":"2015-10-10T22:05:41Z","updated_at":"2015-10-10T22:05:41Z","body":"@pluskid If you create organization, I'll be glad to join. Recently I added [cuRAND.jl](https://github.com/JuliaGPU/CURAND.jl) to JuliaGPU to support stochastic algorithms and currently am in a process of designing common library for unified CPU/GPU array programming - something similar to Theano/Torch7 (we should probably start a separate discussion about it). So if you are looking for people ready to contribute, include me to the list. "}}