8:45 |
Breakfast (D Building) Menu and Details
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9:15 |
Opening Notes
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9:30 |
Keynote
Toby Walsh
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10:30 |
Fairness and transparency in machine learning: Tools and techniques
Andreas Dewes
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TNaaS - Tech Names as a Service
Vincent D. Warmerdam
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Semi-Supervised Bootstrapping of Relationship Extractors with Distributional Semantics
David Soares Batista
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Keynote Q/A
Toby Walsh
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11:15 |
Biases are bugs: algorithm fairness and machine learning ethics
Françoise Provencher
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Polynomial Chaos: A technique for modeling uncertainty
Emily Gorcenski
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Conversational AI: Building clever chatbots
Tom Bocklisch
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Keynote Q/A
Verónica Valeros
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12:00 |
Data Analytics and the new European Privacy Legislation
Amit Steinberg
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Fast Multidimensional Signal Processing using Julia with Shearlab.jl
Héctor Andrade Loarca
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What does it all mean? - Compositional distributional semantics for modelling natural language
Thomas Kober
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12:45 |
Lunch (D Building and outside A238)
Menu and Details
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14:15 |
Ethics in Machine Learning Panel
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15:15 |
Spying on my Network for a Day: Data Analysis for Networks.
Aisha Bello
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Hedging portfolios with Reinforcement Learning (CANCELLED)
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Analysing user comments on news articels with Doc2Vec and Machine Learning classification
Robert Meyer
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Panel Discussion
Q&A
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16:00 |
AI assisted creativity
Roelof Pieters
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When the grassroots grow stronger - 2017 through the eyes of German open data activists
Ulrike Thalheim
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Find the text similiarity you need with the next generation of word embeddings in Gensim
Lev Konstantinovskiy
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16:45 |
Coffee
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17:00 |
Data Science for Digital Humanities: Extracting meaning from Images and Text
Hendrik Heuer
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Large Scale Vandalism Detection in Knowledge Bases
Alexey Grigorev
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Where are we looking? Prediciting human gaze using deep networks.
Oliver Eberle
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17:45 |
Social Networks and Protest Participation: Evidence from 130 Million Twitter Users
Jonathan Ronen
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Machine Learning to moderate ads in real world classified's business
Vaibhav Singh, Jaroslaw Szymczak
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Engage the Hyper-Python - a rattle-through many of the ways you can make a Python program faster
Daniele Rapati
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18:30 |
Closing Notes
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18:45 |
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