Machine Learning Coffee Seminars

About Us

Machine Learning Coffee seminars are weekly seminars held jointly by the Aalto University and the University of Helsinki. The seminars aim to gather people from different fields of science with interest in machine learning. Seminars will be held weekly on Mondays at 9 am - 10 am. The location alternates between Aalto University and the University of Helsinki. At Aalto University, talks will be held in Konemiehentie 2, seminar room T5 and at the University of Helsinki in Kumpula, seminar room Exactum D122 (Gustaf Hällströmin katu 2b), unless otherwise noted. Talks will begin at 9:15 am and coffee will be served from 9:00 am.

Subscribe to our mailing list where seminar topics are announced beforehand.

 

Upcoming Talks (2017)

Give a talk

If you are interested in giving a talk, feel free to send your suggested topic and abstract to mlseminar@hiit.fi

Sep. 25

Computational creativity and machine learning.

Abstract: Computational creativity has been defined as the art, science, philosophy and engineering of computational systems which, by taking on particular responsibilities, exhibit creative behaviours. In this talk I first try to elaborate on what creative responsibilities could be and why they are interesting. I then outline ways in which machine learning can be used to take on some of these responsibilities, helping computational systems become more creative.

Speaker: Hannu Toivonen

Affiliation: Professor of Computer Science, University of Helsinki

Place of Seminar: Aalto University


Oct. 2

Machine learning for Materials Research.

Abstract: In materials research, we have learnt to predict the evolution of microstructure starting with the atomic level processes. We know about defects -- point and extended, -- and we know that these can be crucial for the final structural (and related mechanical and electrical) properties. Often simple macroscopic differential equations, which are used for the purpose, fail to predict simple changes in materials. Many questions remain unanswered. Why a ductile material suddenly becomes brittle? Why a strong concrete bridge suddenly cracks and eventually collapses after serving for tens of years? Why the wall of high quality steels in fission reactors suddenly crack? Or, why the clean smooth surface roughens under applied electric fields? All these questions can be answered, if one peeks in to atom's behavior imagining it jumping inside the material. But how the atoms "choose" where to jump amongst the numerous possibilities in complex metals? Tedious parameterization can help to deal with the problem, but machine learning can provide a better and more elegant solution to this problem.

In my presentation, I will explain the problem at hand and show a few examples of former and current application of Neural Network for calculating the barriers for atomic jumps with the analysis of how well the applied NN worked.

Speaker: Flyura Djurabekova

Affiliation: Department of Physics, University of Helsinki

Place of Seminar: University of Helsinki


Oct. 9

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Place of Seminar: Aalto University


Oct. 16

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Place of Seminar: University of Helsinki


Oct. 23

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Speaker: Aristides Gionis

Affiliation: Professor of Computer Science, Aalto University

Place of Seminar: Aalto University


Oct. 30

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Speaker: Guido Consonni

Affiliation: Professor of Statistics, Universita Cattolica del Sacro Cuore

Place of Seminar: University of Helsinki


Nov. 6

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Place of Seminar: Aalto University


Nov. 13

Learning of Ultra High-Dimensional Potts Models for Bacterial Population Genomics

Abstract: The potential for genome-wide modeling of epistasis has recently surfaced given the possibility of sequencing densely sampled populations and the emerging families of statistical interaction models. Direct coupling analysis (DCA) has earlier been shown to yield valuable predictions for single protein structures, and has recently been extended to genome-wide analysis of bacteria, identifying novel interactions in the co-evolution between resistance, virulence and core genome elements. However, earlier computational DCA methods have not been scalable to enable model fitting simultaneously to 10000-100000 polymorphisms, representing the amount of core genomic variation observed in analyses of many bacterial species. Here we introduce a novel inference method (SuperDCA) which employs a new scoring principle, efficient parallelization, optimization and filtering on phylogenetic information to achieve scalability for up to 100000 polymorphisms. Using two large population samples of Streptococcus pneumoniae, we demonstrate the ability of SuperDCA to make additional significant biological findings about this major human pathogen. We also show that our method can uncover signals of selection that are not detectable by genome-wide association analysis, even though our analysis does not require phenotypic measurements. SuperDCA thus holds considerable potential in building understanding about numerous organisms at a systems biological level.

Speaker: Jukka Corander

Affiliation: Professor of Statistics, University of Helsinki and University of Oslo

Place of Seminar: University of Helsinki


Nov. 20

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Place of Seminar: Aalto University


Nov. 27

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Speaker: Erik Aurell

Affiliation: Professor of Biological Physics, KTH-Royal Institute of Technology

Place of Seminar: University of Helsinki

 

A history of previous talks can be found here.

 

Organizers


Samuel Kaski  Professor of Computer Science, Aalto University
Teemu Roos Associate Professor of Computer Science, University of Helsinki
Homayun Afrabandpey PhD Student, Aalto University

 


Last updated on 19 Sep 2017 by Homayun Afrabandpey - Page created on 3 Dec 2016 by Homayun Afrabandpey