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 on Mondays at 9 am at Aalto University and the University of Helsinki every other week. At Aalto University, talks will be held in Konemiehentie 2, seminar room T5 and at the University of Helsinki in Kumpula, seminar room Exactum D123, 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

Feb. 27

Inverse Modeling in Behavioral Sciences and HCI

Abstract: Can one make deep inferences about a person based only on observations of how she acts? I discuss methodology for inverse modeling in behavioral sciences, where the goal is to estimate a cognitive model from limited behavioral data. Given substantial diversity in people's intentions, strategies and abilities, this is a difficult problem and previously unaddressed. I discuss advances achieved with an approach that combines (1) computational rationality, to predict how a person adapts to a task when her capabilities are known, and (2) Approximate Bayesian Computation (ABC) to estimate those capabilities. The benefit is that model parameters are conditioned on both prior knowledge and observations, which improves model validity and helps identify causes for observations. Inverse modeling methods can advance theory-formation by bringing complex behavior within reach of modeling. This talk is based on on-going collaborations with Antti Kangasraasio, Samuel Kaski, Jukka Corander, Andrew Howes, Kumaripaba Athukorala, Jussi Jokinen, Sayan Sarcar, and Xiangshi Ren.

Speaker: Antti Oulasvirta

Affiliation: Associate Professor, Aalto University

Place of Seminar: University of Helsinki

March. 6

Differentially Private Bayesian Learning

Abstract: Many applications of machine learning for example in health care would benefit from methods that can guarantee data subject privacy. Differential privacy has recently emerged as a leading framework for private data analysis. Differenctial privacy guarantees privacy by requiring that the results of an algorithm should not change much even if one data point is changed, thus providing plausible deniability for the data subjects.

In this talk I will present methods for efficient differentially private Bayesian learning. In addition to asymptotic efficiency, we will focus on how to make the methods efficient for moderately-sized data sets. The methods are based on perturbation of sufficient statistics for exponential family models and perturbation of gradients for variational inference. Unlike previous state-of-the-art, our methods can predict drug sensitivity of cancer cell lines using differentially private linear regression with better accuracy than using a very small non-private data set.

Speaker: Antti Honkela

Affiliation: Assistant Professor, University of Helsinki

Place of Seminar: Aalto University

NOTE: Exceptially this talk will be held at seminar room T6

March. 20

Semi-supervised Deep Learning


Speaker: Harri Valpola

Affiliation: CEO of the Curious AI Company

Place of Seminar: Aalto University


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 20 Feb 2017 by Homayun Afrabandpey - Page created on 3 Dec 2016 by Homayun Afrabandpey