This list is intended to introduce some of the tools of Bayesian statistics and machine learning that can be useful to computational research in cognitive science.
Mahout currently has
Collaborative Filtering
User and Item based recommenders
K-Means, Fuzzy K-Means clustering
Mean Shift clustering
Dirichlet process clustering
Latent Dirichlet Allocation
Singular value decomposition
Parallel Frequent Pattern mining
Complementary Naive Bayes classifier
Random forest decision tree based classifier
High performance java collections (previously colt collections)
A vibrant community
and many more cool stuff to come by this summer thanks to Google summer of code
I'm interested in machine learning techniques (graphical models, kernel methods) applied to text understanding (entity and relation extraction, coreference resolution, document classification and clustering, confidence prediction, social network analysis, data mining).
D. Kingma, and J. Ba. (2014)cite arxiv:1412.6980Comment: Published as a conference paper at the 3rd International Conference for Learning Representations, San Diego, 2015.
A. Coates, H. Lee, and A. Ng. Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, volume 15 of JMLR Workshop and Conference Proceedings, page 215--223. JMLR W&CP, (2011)