ENBIS-18 in Nancy2 – 25 September 2018; Ecoles des Mines, Nancy (France) Abstract submission: 20 December 2017 – 4 June 2018
Post-Conference Event: Joint ECAS-ENBIS 1-Day Summer School6 September 2018, 08:30 – 17:00
This full-day ENBIS-18 post-conference workshop is a joint initiative from ENBIS and ECAS.
It will be given by Stéphane Canu and Matteo Fasiolo and can be booked here.
Introduction to Deep Learning
This course is an introduction to deep learning. We will present deep learning from a historical perspective ranging from the formal neuron up to recent models from both supervised and unsupervised learning. Special emphasis will be on applications domains (image, audio, NLP, games), architectures design, generative learning and non-convex optimization.
An Introduction to Generalized Additive Models
Generalized Additive Models (GAMs) models are an extension of traditional parametric regression models, which have proved highly useful for both predictive and inferential purposes in a wide variety of scientific and commercial applications. One reason behind the popularity of GAMs is that they strike an interesting balance between flexibility and interpretability, while being able to handle large data sets. The mgcv R package is arguably the state-of-the-art tool for fitting such models, hence the first half of this tutorial will introduce GAMs and mgcv, in the context of electricity demand forecasting. The second part of the tutorial will show how traditional GAMs can be extended to quantile GAMs, and how the latter can be fitted using the qgam R package. By the end of the tutorial the attendees should be able to build, fit and visualize traditional or quantile GAM models, using a combination of the mgcv, qgam and mgcViz R packages. This tutorial is aimed at a broad audience of statistical modellers, interested in using GAMs for predictive or inferential purposes. The models which will be presented in the tutorial have a very wide range of applicability, hence they should be of interest to practitioners in business intelligence, ecology, linguistics, epidemiology and geoscience to name a few.