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.

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Upcoming Talks (2017)

Give a talk

If you are interested in giving a talk, feel free to send your suggested topic and abstract to

Nov. 20

Towards Intelligent Exergames

Abstract: Exergames - video games that require physical activity - hold promise of solving the societal hard problem of motivating people to move. At the same time, artificial intelligence and machine learning are transforming how video games are designed, produced, and tested. Work combining both computational intelligence and exergames is sparse, however. In my talk, I delineate the challenges, opportunities, and my group's research towards intelligent exergames, building on our previous research on both exergame design (e.g., Augmented Climbing Wall, Kick Ass Kung-Fu) and intelligent control of embodied simulated agents. Video and examples:

Speaker: Perttu Hämäläinen

Affiliation: Professor of Computer Science, Aalto University

Place of Seminar: Aalto University

Nov. 27

Correlation-Compressed Direct Coupling Analysis

Abstract: Direct Coupling Analysis (DCA) is a powerful tool to find pair-wise dependencies in large biological data sets. It amounts to inferring coefficients in a probabilistic model in an exponential family, and then using the largest such inferred coefficients as predictors for the dependencies of interest. The main computational bottle-neck is the inference. As described recently by Jukka Corander in this seminar series DCA has be done on bacterial whole-genome data, at the price of significant compute time, and investment in code optimization.

We have looked at if DCA can be speeded up by first filtering the data on correlations, an approach we call Correlation-Compressed Direct Coupling Analysis (CC-DCA). The computational bottle-neck then moves from DCA to the more standard task of finding a subset of most strongly correlated vectors in large data sets. I will describe results obtained so far, and outline what it would take to do CC-DCA on whole-genome data in human and other higher organisms.

This is joint work with Chen-Yi Gao and Hai-Jun Zhou, available as arXiv:1710.04819.

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.



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 16 Nov 2017 by Homayun Afrabandpey - Page created on 3 Dec 2016 by Homayun Afrabandpey