6. Conclusion
Unsupervised statistical learning is a relatively recent discipline, which is still the subject of fundamental research and regularly gives rise to major innovations. Although this type of learning is more difficult to grasp and implement than supervised learning, and the techniques used are still evolving, it is already leading to spectacular successes, particularly in natural language processing and multimedia content generation.
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Glossary
Bibliography
Software tools
For computations that do not involve deep learning and that deal with data volumes that do not require the use of distributed computing, the two reference software tools are scikit-learn and R
The Spark Mlib library adapts the main machine learning algorithms (excluding deep learning) to a distributed environment, enabling the processing of very large volumes of data.
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International Conference on Learning Representations ( https://iclr.cc/ )
Conference on Neural Information Processing Systems ( https://nips.cc/ )
Conference on Computer Vision and...
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