Algodan > Machine Learning

The senior members of professor Jyrki Kivinen's team are Kivinen himself and Juho Rousu. In particular, the team studies theoretically well-founded methods and rigorous performance bounds for them. This includes analyzing the methods both in the classical statistical setting and in an online setting, where many of the classical assumptions can be avoided. The other strong focus of the team is applications in bioinformatics.


Machine Learning

The Machine Learning subgroup develops and analyses theoretically well-founded general-purpose machine learning methods and applies them in selected areas which have high potential for co-operation with other groups. For example, the group has developed the Sinuhe machine translation system.

Kernel Machines, Pattern Analysis and Computational Biology

The group develops machine learning methods, models and tools for computational sciences, in particular computational biology. The methodological backbone of the group is kernel methods and regularized learning. The group particularly focusses in learning with multiple and structured targets, multiple views and ensembles. Applications of interest in computational biology include protein function and interaction prediction as well as molecular classification and identification.