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 D123, unless otherwise noted. Talks will begin at 9:15 am and coffee will be served from 9:00 am.

 

Upcoming Talks (2017)

Give a talk

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

Jan. 30

Likelihood-free Inference and Predictions for Computational Epidemiology

Abstract: Simulator-based models often allow inference and predictions under more realistic assumptions than those employed in standard statistical models. For example, the observation model for an underlying stochastic process can be more freely chosen to reflect the characteristics of the data gathering procedure. A major obstacle for such models is the intractability of the likelihood, which has to a large extent hampered their practical applicability. I will discuss recent advances in likelihood-free inference that greatly accelerate the model fitting process by exploiting a combination of machine learning techniques. Applications to several novel models in infectious disease epidemiology are used to illustrate the potential offered by this approach.

Speaker: Jukka Corander

Affiliation: Professor, University of Helsinki and University of Oslo

Place of Seminar: University of Helsinki


Feb. 6

Toward Perfect Density Estimation

Abstract: We start by addressing a most simple problem, estimation of a one dimensional density function, and argue that despite of the apparent simplicity of the problem, it is surprisingly difficult to solve it in a holistic manner that is both computationally feasible and theoretically justifiable without strong distributional or other assumptions. We demonstrate how the information-theoretic MDL framework can be used for reaching this goal (almost) perfectly, and show how this simple setup gives interesting perspectives on the fundamental concepts in probabilistic modelling and statistical inference. We also discuss ideas for extending the framework to more complex models with additional practical applications.

Speaker: Petri Myllymaki

Affiliation: Professor, University of Helsinki

Place of Seminar: Aalto University


Feb. 13

Speaker: Matti Pirinen

Affiliation: Academy Research Fellow, Institute for Molecular Medicine Finland, University of Helsinki

Place of Seminar: University of Helsinki


Feb. 20

Compressed Sensing for Semi-Supervised Learning From Big Data Over Networks

Abstract: In this talk I will present some of our most recent work on the application of compressed sensing to semi-supervised learning from massive network-structured datasets, i.e., big data over networks. We expect the user of compressed sensing ideas to be game-changing for machine learning from big data in a similar manner as it was for digital signal processing. In particular, I will present a sparse label propagation algorithm which efficiently learn from large amounts of network-structured unlabeled data by leveraging the information provided by a few initially labelled training data points. This algorithm is inspired by compressed sensing recovery methods and allows for a simple sufficient condition on the network structure which guarantees accurate learning.

Speaker: Alexander Jung

Affiliation: Assistant Professor, Aalto University

Place of Seminar: Aalto University

 

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 23 Jan 2017 by Homayun Afrabandpey - Page created on 3 Dec 2016 by Homayun Afrabandpey