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.

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

May. 22

On Priors and Bayesian Variable Selection in Large p, Small n Regression

Abstract: The Bayesian approach is well known for using priors to improve inference, but equally important part is the integration over the uncertainties. I first present recent development in hierarchical shrinkage priors for presenting sparsity assumptions in covariate effects. I then present a projection predictive variable selection approach, which is a Bayesian decision theoretical approach for variable selection which can preserve the essential information and uncertainties related to all variables in the study. I also present recent excellent experimental results and easy to use software.

Speaker: Aki Vehtari

Affiliation: Professor of Computer Science, Aalto University

Place of Seminar: University of Helsinki

May. 29

Graphics Meets Vision Meets Machine Learning

Abstract:

Speaker: Jaakko Lehtinen

Affiliation: Professor of Computer Science, Aalto University

Place of Seminar: Aalto University

Jun. 5

Abstract:

Speaker: Jarno Vanhatalo

Affiliation: Professor of Statistics, University of Helsinki

Place of Seminar: University of Helsinki

Jun. 12

Learning Data Representation by Large-Scale Neighbor Embedding

Abstract: Machine learning, the state-of-the-art data science, has been increasingly influencing our life. Encoding data in a suitable vector space is the fundamental starting point for machine learning. A good vector coding should respect the relations among the data items. However, conventional methods that preserve pairwise or higher order relationship are very slow and consequently they can handle only small-scale data sets. We have been developing a family of unsupervised methods called large-scale Neighbor Embedding (NE) which substantially accelerate the vector coding. Our method can thus learn low-dimensional vector representation for mega-scale data according to their neighborhoods in the original space. With our efficient algorithms and a wealth of neighborhood information, Neighbor Embedding significantly outperforms small-scale NE and many other existing approaches for learning data representation. Besides generic feature extraction, our work also delivers two important tools as special cases of Neighbor Embedding for data visualization and cluster analysis, which scales up these applications by an order of magnitude and enables the current-sized visualization and clustering for interactive use. Because neighborhood information is naturally and massively available in many areas, our method has wide applications as a critical component in scientific research, next-generation DNA sequence analysis, natural language processing, educational cloud, financial data analysis, market studies, etc.

Speaker: Zhirong Yang

Affiliation: Department of Computer Science, Aalto University

Place of Seminar: Aalto University

NOTE: Exceptionally this talk will be held in seminar room T2.

 

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