Foula Vagena – Deep Learning: An overview using Multi Layer Perceptrons (MLPs)

2021, diiP, Seminars + Hands-On Workshops

Foula Vagena
June 2, 4pm
online (zoom)

 

Abstract

Deep Learning (DL for short) is a field of machine learning that is concerned with algorithms based on (artificial) neural networks and representation learning. The quintessential example of a deep learning model is the feedforward deep network or multilayer perceptron (MLP). In this tutorial we will provide an overview DL and present its main components (e.g. tensors, layered composition of models/functions). We will focus on MLPs and using that we will delineate and explain the main steps/concepts for DL-based modeling. The tutorial will conclude with an illustrative hands-on example of MLP-supported regression and classification models.
The hands-on workshop will focus on MLP supported regression + classification examples.

 

Dr Foula Vagena
(Université Paris Cité, diiP)
Zografoula Vagena is a research associate at the Data Intelligence Institute of Paris (diiP) and affiliated with the Université Paris Cité. She has been a data science researcher and practitioner for over ten years. She has worked on different analytics problems including forecasting, image processing, graph analytics, multidimensional data analysis, text processing, recommendation systems, sequential data analysis and optimization within various fields such as transportation, healthcare, retail, finance/insurance and accounting. She has also performed research in the intersection of data management and analytics, and was a primary contributor of the MCDB/SimSQL systems that blended data management with Bayesian statistics. She holds a PhD in data management from the University of California, Riverside.

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