Machine Learning Coffee Seminars

Machine Learning Coffee Seminars2019-01-14T11:39:04+00:00

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 T6 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 (2019)

February 2019

Securing Pedestrians in Autonomous Traffic Ecosystems of Smart Cities

Date: February 18, 2019

Abstract: Future smart cities must be based on sustainable principles; they must provide improved quality of life for all citizens, be safe and as emission-free as possible. This goal requires radical reforms in the traffic, namely creation of automated ground vehicle ecosystem and relocating parts of the transportation of goods, probably also human, to the airspace by using Unmanned Aerial Vehicles (UAVs). Pedestrians and bicyclists must be included in traffic monitoring and controlling actions, a fact that has been largely neglected so far in the discussion
about automated traffic. All these goals demand development of sophisticated spatiotemporal data analysis methods. In order to implement a functional traffic ecosystem assuring safe cooperation of
all these actors, knowledge of their position, ability to predict their movements, effects caused by changes of the operation environment and capability to fuse all this information together is crucial.

The most challenging actors from the navigation perspective are pedestrians. Their motion is unrestricted, they spend a big portion of time indoors and they have strict demands for navigation equipment. This talk will give a glimpse to the research goals of the Spatiotemporal Data Analysis research group and will look a bit more into a specific application of infrastructure-free pedestrian navigation and especially user motion recognition via machine learning to improve the navigation result.

Speaker: Laura Ruotsalainen

Affiliation: Professor of Computer Science, University of Helsinki

Place of Seminar: Seminar Room T6, Konemiehentie 2, Aalto University

Learning From Electronic Health Records: From Temporal Abstractions to Time Series Interpretability

Date: February 25, 2019

Abstract: The first part of the talk will focus on data mining methods for learning from Electronic Health Records (EHRs), which are typically perceived as big and complex patient data sources. On them, scientists strive to perform predictions on patients’ progress, to understand and predict response to therapy, to detect adverse drug effects, and many other learning tasks. Medical researchers are also interested in learning from cohorts of population-based studies and of experiments. Learning tasks include the identification of disease predictors that can lead to new diagnostic tests and the acquisition of insights on interventions. The talk will elaborate on data sources, methods, and case studies in medical mining.
The second part of the talk will tackle the issue of interpretability and explainability of opaque machine learning models, with focus on time series classification. Time series classification has received great attention over the past decade with a wide range of methods focusing on predictive performance by exploiting various types of temporal features. Nonetheless, little emphasis has been placed on interpretability and explainability. This talk will formulate the novel problem of explainable time series tweaking, where, given a time series and an opaque classifier that provides a particular classification decision for the time series, the objective is to find the minimum number of changes to be performed to the given time series so that the classifier changes its decision to another class. Moreover, it will be shown that the problem is NP-hard. Two instantiations of the problem will be presented. The classifier under investigation will be the random shapelet forest classifier. Moreover, two algorithmic solutions for the two problem instantiations will be presented along with simple optimizations, as well as a baseline solution using the nearest neighbor classifier.

Speaker: Panagiotis Papapetrou

Affiliation: Professor of Computer Science, Stockholm University

Place of Seminar: Lecture Hall Exactum D122, University of Helsinki

March 2019

Date: March 4, 2019


Speaker: Mark van Gils

Affiliation: Research Professor, VTT Technical Research Center of Finland

Place of Seminar: Seminar Room T6, Konemiehentie 2, Aalto University

Recent Advances in Aalto ASR Group

Date: March 11, 2019

Abstract: I will introduce the current research in my automatic speech recognition (ASR) group at Aalto University. It includes the new acoustic and language models by which we won the MGB ASR Challenge 2017 and the new applications such as automatic pronunciation evaluation and verbal description of audiovisual data.

Dr. Mikko Kurimo is a professor in speech and language processing at Aalto University, Finland. He has lead Aalto’s speech recognition research group since 2000 as well as several national and international research projects. His work is internationally best known for unsupervised subword language modeling for morphologically complex languages such as Finnish, Estonian and Arabic.

Speaker: Mikko Kurimo

Affiliation: Professor of Speech and Language Processing, Aalto University

Place of Seminar: Lecture Hall Exactum D122, University of Helsinki

Date: March 18, 2019

Abstract: TBD

Speaker: Mikko Tolonen

Affiliation: Professor of Digital Humanities, University of Helsinki

Place of Seminar: Seminar Room T6, Konemiehentie 2, Aalto University

Date: March 25, 2019

Abstract: TBD

Speaker: TBD

Place of Seminar: Lecture Hall Exactum D122, University of Helsinki


Samuel Kaski  Professor of Computer Science, Aalto University
Teemu Roos Professor of Computer Science, University of Helsinki
Homayun Afrabandpey Researcher, Aalto University