Machine Learning Coffee Seminars2019-09-18T11:05:09+02:00

About Us

Machine Learning Coffee seminars are weekly seminars organized by Finnish Center for Artificial Intelligence (FCAI) and are 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.

Please see FCAI’s YouTube channel for video recordings of past talks.

Subscribe to our mailing list where seminar topics are announced beforehand.

Upcoming Talks (2019)

November 2019

Machine learning in drone remote sensing

Date: November 18, 2019

Abstract: Utilization of unmanned aerial vehicles, drones, has increased explosively during the past couple of years. Integrating analytics and actuation either utilizing individual drones, drone teams or drone swarms will enable robotization of various environmental operations in the future. This presentation focuses on drone based remote sensing in agricultural and forestry applications. Drones become intelligent when coupled with sensors and analytics. Imaging sensors (color, hyperspectral and multispectral cameras) enable exploration of objects’ 3D structure, and reflection and absorption characteristics. Essential data preprocessing steps are georeferencing, 3D object reconstruction and radiometric calibration. Machine learning plays a fundamental role in building the analytics process. The physical observations measured in field or in laboratory are related to the remote sensing observations and models are built to extract information. Then in analytics applications remote sensing data are fed to the analyzer and the information are extracted utilizing the models. This presentation deals with remote sensing sensor, data calibration and machine learning and presents selected case studies in forest inventory, tree health analysis and grass quality and quantity assessment.

Bio: Dr. Eija Honkavaara is Research Manager at the Finnish Geospatial Research Institute in the National Land Survey (FGI) and Docent at the Aalto University in the field of Automatic Systems in Photogrammetry. Her current research focuses on drone based geoinformation and AI technologies for environmental remote sensing. The DroneFinland-team led by Dr Honkavaara has developed several generations of high-performance drone remote sensing systems with cameras, hyperspectral sensors, analytics applications as well as inhouse data processing chains. She has a wide international research network, is a leader of working group “Hyperspectral Imaging Applications” in the International Society for Photogrammetry and Remote Sensing (ISPRS) and has led several national and international projects on UAV remote sensing.

Speaker: Eija Honkavaara
Affiliation: Finnish Geospatial Research Institute, FGI

Place of Seminar: Lecture Hall T5, Konemiehentie 2, Aalto University

Networked Exponential Families for Big Data over Networks

Date: November 25, 2019

Abstract: The data arising in many important applications consists of high-dimensional data points that are related by an intrinsic network structure. We introduce networked exponential families to jointly leverage the information in the topology as well as the high-dimensional attributes (features or labels) of networked data points. Networked exponential families provide a flexible probabilistic model for heterogeneous datasets with intrinsic network structure. These models can be learnt efficiently using network Lasso, which implicitly pools or clusters the data points according to the intrinsic network structure and the local likelihood. The resulting method can be formulated as a non-smooth convex optimization problem which we solve using a primal-dual splitting method.
This primal-dual method is appealing for big data applications as it can be implemented as a highly scalable message-passing algorithm.
Paper: https://arxiv.org/abs/1905.09056

Bio: Alexander Jung received the Diplom-Ingenieur and Dr. techn. degrees in electrical engineering/signal processing from Vienna University of Technology, Vienna, Austria, in 2008, and 2011, respectively. After Post-Doc stays at TU Vienna and ETH Zurich, he joined Aalto in 2015. He is currently assistant professor for machine learning within the Department of Computer Science at Aalto University. His research interests are in statistical signal processing and machine learning for big data with emphasis on sparse models as well as trade-offs between accuracy and computational complexity of learning algorithms. He has taught the main courses on machine learning and artificial intelligence attracting several thousands of students from and beyond Aalto.

Speaker: Alex Jung
Affiliation: Assistant Professor of Computer Science, Aalto University

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

December 2019

TBA

Date: December 02, 2019

Speaker: Keijo Tapio Heljanko
Affiliation: Professor of Computer and information Sciences, Helsinki University

Place of Seminar: Lecture Hall T5, Konemiehentie 2, Aalto University

Organizers

Samuel Kaski Professor of Computer Science, Aalto University
Laura Ruotsalainen Professor of Computer Science, University of Helsinki
Khaoula El Mekkaoui Doctoral Student, Aalto University