# implement a machine learning library

For modern datascience we need machine learning. Hence for .NET a modern machine learning library would be very welcome, especially one with a good F# interface. It should contain the most used standard algorithms & models like

- clustering: k-means, spectral clustering, kernel k-means, gaussian mixture, ...

- classification & regression: (regularized) generalized linear models , neural networks (classic & deep learning), kernel models (SVM, kernel logistic regression,...), probabilistic (max likelihood) & Bayesian methods/graphical models/gaussian processes, decision trees & random forests,...

- ensemble methods (bagging, boosting,...)

- dim reduction (SVD, kernel SVD, manifold learning, sparse representations (L1 regularization),...)

- probabilistic programming (cf. infer.NET) ?

- ...

Also applications to specific domains would be nice like

- recommendations!!!

- text mining/information retrieval (calculate tf-idf,LSA, LDA,...)

- network analysis

- adaptive DSP/compression/image processing/computer vision

- ...

As a start we could leverage accord.Net and build further on that.

as a side project one could have library for optimization that could be used by the ML lib & directly:

- gradient descient

- linear/quadratic/ convex programming

- other methods like genetic algos/evolutionary computing,simulated annealing,...

**54**votes