The 17th Geilo Winter School in eScience will be held from Sunday January 15th to Friday January 20th 2017 in Geilo, Norway.

This years topic will be machine learning, deep learning, and data analytics. The digitalization wave has brought with it an explosion in the variety and number of data sources, ranging from various physical sensors to text, video, audio, social networks, and simulation results. Traditional techniques such as simple statistics has shown to be insufficinent with such complex and massive data, but developments within artificial intelligence and machine learning has in the recent years shown great promise. Techniques within artificial neural networks, deep learning and data analytics are now able to extract knowledge hidden in vast amounts of data in a way on a level we have not seen before.

Algorithms within artificial intelligence are often applicable to a wide range of application areas, and the aim of this winter school is that participants should learn concepts, techniques, and algorithms for use in their own research.

Download flyer here.

JV: Joaquin Vanschoren
1: Introduction to ML in Python
2: Classification and regression
3: Data preprocessing and dimensionality reduction
Slides: Python intro, Scientific python, scikit-learn, OpenML, OpenML-overview, supervised learning, model selection.
Github repository

NLH: Nils Lid Hjort
1: Model Selection and Model Averaging
2: Confidence Distributions
3: Bayesian Nonparametrics

DD: Devdatt Dubhashi
1: Word Embeddings
2: Summarization
3: Word senses

RJ: Robert Jenssen, Michael Kampffmeyer, Filippo M. Bianchi
1: Deep learning by convolutional networks (Jenssen, Kampffmeyer)
2: Kernel methods and spectral clustering (Jenssen)
3: Recurrent neural networks (Bianchi)

MT: Mark Tibbetts
1: Industrial use-cases and workflows for ML
Simple regression problem, Downtime classification problem

AB: André R. Brodtkorb
Welcome & introduction
Summary

This years school will give you an introduction to machine learning, deep learning, and data analytics from experts in the field. Over the week-long school, the lecturers will cover topics including deep learning, convolutional networks, kernel methods, spectral clustering, recurrent neural networks, big data, cognitive computing, and more. The school will also include a tutorial on using scikit-learn from Python to use machine learning techniques.

Robert Jenssen, UiT, Norway
Robert Jenssen is Associate Professor at the Arctic University of Norway (UiT, Tromsø, Norway) Professor II at the Norwegian Computing Center (NR, Oslo, Norway), and Senior Researcher at the Norwegian Center on Integrated Care and Telemedicine (University Hospital of North Norway). At the Arctic University of Norwa he directs the Machine Learning @UiT Lab, with a focus on using information theoretic learning, kernel methods, graph spectral methods, and big data algorithms with deep learning.
Webpage

Devdatt Dubhashi, Chalmers, Sweden
Devdatt Dubhashi is Professor at Chalmers University of Technology (Gothenburg, Sweden), where he leads the Algorithms, Machine Learning, and Computational Biology group. He has a PhD from Cornell Unviersity (USA), has been a postdoc at Max Planck Institute (Germany), and  his research interests include design and analysis of randomized algorithms, machine learning for Big Data, and computational biology. He has previously served as an expert for OECD on the Data Driven Innovation report and on a Big Data panel.
Webpage

Joaquin Vanschoren, Eindhoven University of Technology, Netherlands
Joaquin Vanschoren is Assitant Professor of Machine Learning at the Eindhoven Unviersity of Technology (TuE, Eindhoven, Netherlands). He is the founder of OpenML.org, a collaborative machine learning platform where scientists automatically can log and share data, code and experiments, and the platform automatically learns from the data to help people perform machine learning better. His research interests also include large-scale data analysis of data including social, streams, geo-spatial, sensors, network, and text.
Webpage

Nils Lid Hjort, University of Oslo, Norway
Nils Lid Hjort is Professor at the University of Oslo. He is the author of several books on topics including Bayesian nonparametrics, model selection and model averaging, and confidence distributions. He has served on the board of several journals, and an elected member of the Norwegian Academy of Science and Letters. He is currently also leading the project FocuStat: Focus Driven Statistical Inference With Complex Data, funded by the Research Council of Norway.
Webpage

Filippo M. Bianchi, UiT, Norway
Filippo Bianchi is a post doc at the University of Tromsø. He received his B.Sc. in Computer Engineering (2009), M.Sc. in Artificial Intelligence and Robotics (2012) and PhD in Machine Learning (2015) from the "Sapienza" University, Rome. Filippo has worked 2 years as research assistant at the Computer Science department at Ryerson University, Toronto, and his research interests in machine learning and pattern recognition include graph and sequence matching, clustering, classification, reservoir computing, deep learning and data mining.
 

Mark Tibbetts, Arundo Analytics Inc, Norway
Mark Tibbetts is a data scientist at Arundo with a background in high energy physics and over ten years experience analyzing large datasets using advanced statistical techniques. He has a Ph.D. from Imperical College London, and has been a post doc and researcher at Berkeley Lab working with the ATLAS experiment at CERN. His research interests include the application of machine learning methods to industrial big data, and he aims to deliver high impact contributions to large scale data analysis and software engineering projects.
 

Michael Kampffmeyer, UiT, Norway
Michael Kampffmeyer is a Ph.D. student at the Arctic University of Norway, where he works on applications and development of deep learning algorithms in a joint effort with researchers at the Norwegian Computing Center in Oslo, Norway. Michael studies issues related to transfer learning, the handling of different image resolutions and multi-modality. He is also interested in the combination of CNNs and unsupervised learning.
Webpage

Johannes Beil, Monica J. Emerson, Anna Puig-Molina, Henning O. Sørensen, Robert K. Feidenhans'l
3D X-Ray Machine Vision for Better Catalysts

Aliaksandr Hubin, Geir Storvik
Efficient mode jumping MCMC for Bayesian variable selection and model averaging in GLMM

Aina Juell Bugge, Stuart Clark, Jan Erik Lie, Jan Inge Faleide
Semi-automatic interpretation of the Earth's interior

Allan P. Engsig-Karup, Steffen Holmslykke, Søren Lindegaard Grubov
Finding Common Root Causes in Autotest Results (Applied Machine Learning techniques)

André Ourednik, Peter Fleer, Stefan Nellen
Seeing Law as Quantities and Graphs Explorations in Digital History

Andrea Raffo, Oliver Barrowclough, Heidi Dahl, Tor Dokken, Michael Floater and Georg Muntingh
Locally refined approximate implicitisation for design and manufacturing

Carlos González-Gutiérrez, Mario Martinez-Zarzuela, F. J. de Cos Juez, F. J. Díaz-Pernas
Analyzing the performance of a tomographic reconstructor with different neural networks frameworks

Céline Cunen, Gudmund Hermansen, Nils Lid Hjort
When did author B take over for author A? Confidence Distributions for Change-Points

Chaoran Fan, Helwig Hauser
Fast and accurate Mahalanobis brushing in scatterplots, optimized on the basis of a user study

Erich Suter, Sergey Alyaev
Proactive geosteering workflow for enhanced oil recovery

Flávia Dias Casagrande, Evi Zouganeli
The Assisted Living Project: Responsible innovations for dignified lives at home for persons with Mild Cognitive Impairment or dementia

Femke B. Gelderblom and E. M. Viggen
Speech Enhancement with Deep Learning

Erlend Hodneland, Sergej Stoppel, Astri J. Lundervold, Helwig Hauser, and Arvid Lundervold
Predicting age from brain MR imaging data using algorithms for machine learning

Arvid Lundervold, Eivind A. Valestrand, Alexander S. Lundervold, Trygve Hausken
Predicting irritable bowel syndrome (IBS) from brain MR imaging data using machine learning

Min Shi, Hong Li
Intelligent Model Calibration using Multi-objective Optimization

Mohammed Sourori
Runtime Exploitation of application dynamism for energy-efficient exascale computing

Ole-Johan Skrede, Fritz Albregtsen, Håvard E. Danielsen
Cell nuclei segmentation using deep convolutional neural networks

Jean Rabault, Jostein Kolaas
Performing Particle Image Velocimetry using Artificial Neural Networks

Sigmund Akselsen
Professorship in AI and Machine Learning

Yaman Umuroglu, Nicholas J. Fraser, Giulio Gambardella, Michaela Blott, Philip Leong, Magnus Jahre and Kees Vissers
FINN: A Framework for Fast, Scalable Binarized Neural Network Inference on Reconfigurable Logic