Deep Learning has revolutionised Pattern Recognition and Machine Learning. It is about credit assignment in adaptive systems with long chains of potentially causal links between actions and consequences.
Mahout currently has
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
Platform for sharing and evaluation of intelligent algorithms. Data mining data, experiments, datasets, performance analysis, data repository, challenges. Research and applications, prediction. Data mining and machine learning
The Knowledge Discovery Machine Learning (KDML) group focuses on the neighboring subfields of computer science known as knowledge discovery in databases (KDD, sometimes referred to simply as data mining) and machine learning (ML). For us, these fields include on the one hand the automated analysis of large data sets using intelligent algorithms that are capable of extracting from the collected data hidden knowledge in order to produce models that can be used for prediction and decision making. On the other hand, they also include algorithms and systems that are capable of learning from experience and adapting to their environment or their users.
D. Nguyen, N. Smith, and C. Rosé. Proceedings of the 5th ACL-HLT Workshop on Language Technology for Cultural Heritage, Social Sciences, and Humanities, page 115--123. Stroudsburg, PA, USA, Association for Computational Linguistics, (2011)
X. Zhang, and Y. LeCun. (2015)cite arxiv:1502.01710Comment: This technical report is superseded by a paper entitled "Character-level Convolutional Networks for Text Classification", arXiv:1509.01626. It has considerably more experimental results and a rewritten introduction.