History

Jan. 9

Unsupervised Machine Learning for Matrix Decomposition

Abstract: Unsupervised learning is a classical approach in pattern recognition and data analysis. Its importance is growing today, due to the increasing data volumes and the difficulty of obtaining statistically sufficient amounts of labelled training data. Typical analysis techniques using unsupervised learning are principal component analysis, independent component analysis, and cluster analysis. They can all be presented as decompositions of the data matrix containing the unlabeled  samples. Starting from the classical results, the author reviews some advances in the field up to the present day.

Speaker: Erkki Oja

Affiliation: Professor Emeritus, Aalto University

Place of Seminar: Aalto University


Jan. 16

Probabilistic Programming: Bayesian Modeling Made Easy

Abstract: Probabilistic models are principled tools for understanding data, but difficulty of inference limits the complexity of models we can actually use. Often we need to develop specific inference algorithms for new models (which might take months), and need to restrict ourselves to tractable model families that might not match our beliefs about the data. Probabilistic programming promises to fix this, by separating the model description from the inference: With probabilistic programming languages we can specify complex models using a high-level programming language, letting a black-box inference engine take care of the tricky details. This talk covers the basic idea of probabilistic programming and discusses how well its promises hold now and in the future.

Speaker: Arto Klami

Affiliation: Academy Research Fellow, University of Helsinki

Place of Seminar: University of Helsinki


Jan. 23

Metabolite Identification Through Machine Learning

Abstract: Identification of small molecules from biological samples remains a major bottleneck in understanding the inner working of biological cells and their environment. Machine learning on data from large public databases of tandem mass spectrometric data has transformed this field in recent years, witnessing an increase of identification rates by 150%. In this presentation, I will outline the key machine learning methods behind this development: kernel-based learning of molecular fingerprints, multiple kernel learning, structured prediction as well as some recent advances.

Speaker: Juho Rousu

Affiliation: Associate Professor, Aalto University

Place of Seminar: Aalto University

 


Last updated on 23 Jan 2017 by Homayun Afrabandpey - Page created on 12 Dec 2016 by Homayun Afrabandpey