I suggest you ...

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,...

51 votes
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