Advances and fundamentals in ML
As in any science, fertility in Machine Learning also comes from cross-pollination. Keeping an eye open for interesting development across as many of the fields in ML as possible, we hope to identify developments with great potential. This includes techniques in optimisation, model evaluation and selection, function approximation and many other topics. At the same time, many fundamental concepts in ML are subtle, require careful analysis and must be often revisited and reexamined for new insights and developments.