Ethics in Machine Learning Panel

Ethics in Machine Learning Panel Q&A

Verónica Valeros | Keynote – The Future of Cybersecurity Needs You, Here is Why.

Verónica Valeros – Keynote Q&A

Toby Walsh | Keynote – Are many of your worries about AI wrong?

Toby Walsh – Keynote Q&A

Barbara Plank | Keynote – Natural Language Processing: Challenges and Next Frontiers

Barbara Plank – Keynote Q&A

Radovan Kavicky – Data Science & Data Visualization in Python

Dr. Kristian Rother – Best Practices for Debugging

Daniele Rapati – Engage the Hyper-Python

Oliver Eberle – Where are we looking? Predicting human gaze using deep networks.

Lev Konstantinovskiy – Text similiarity with the next generation of word embeddings in Gensim

Robert Meyer – Analysing user comments with Doc2Vec and Machine Learning classification

Thomas Kober – Compositional distributional semantics for modelling natural language

Tom Bocklisch – Conversational AI: Building clever chatbots

David Soares Batista – Semi-Supervised Bootstrapping of Relationship Extractors

Abhishek Thakur – Is That a Duplicate Quora Question?

Nick Radcliffe – Developments in Test-Driven Data Analysis

Matti Lyra – Evaluating Topic Models

Tal Perry – A word is worth a thousand pictures: Convolutional methods for text

Miroslav Batchkarov – Gold standard data: lessons from the trenches

Adrin Jalali – The path between developing and serving machine learning models.

James Powell – Advanced Metaphors in Coding with Python

Gerrit Gruben – Leveling up your Jupyter notebook skills

David Higgins, Robert Schwarz – Introduction to Julia for Scientific Computing and Data Science

Stephen Simmons – Pandas from the Inside / "Big Pandas"

Alexander Hendorf – Introduction to Data-Analysis with Pandas

Bhargav Srinivasa Desikan – Topic Modelling (and more) with NLP framework Gensim

Jonathan Ronen – Social Networks and Protest Participation: Evidence from 130 Million Twitter Users

Hendrik Heuer – Data Science for Digital Humanities: Extracting meaning from Images and Text

Roelof Pieters – AI assisted creativity

Aisha Bello – Spying on my Network for a Day: Data Analysis for Networks

Françoise Provencher – Biases are bugs: algorithm fairness and machine learning ethics

Andreas Dewes – Fairness and transparency in machine learning: Tools and techniques

Irina Vidal Migallon – Deep Learning for detection on a phone

Carlotta Schatten – Towards Pythonic Innovation in Recommender Systems

Florian Wilhelm, Arnab Dutta – “Which car fits my life?” – mobile.de’s approach to recommendations

Stefan Otte – On Bandits, Bayes, and swipes: gamification of search

Vaibhav Singh, Jaroslaw Szymczak – Machine Learning to moderate ads

Alexey Grigorev – Large Scale Vandalism Detection in Knowledge Bases

Ulrike Thalheim – Open Data Use Cases

Volodymyr (Vlad) Kazantsev – Clean Code in Jupyter notebooks, using Python

Héctor Andrade Loarca – Fast Multidimensional Signal Processing using Julia with Shearlab.jl

Emily Gorcenski – Polynomial Chaos: A technique for modeling uncertainty

Vincent D. Warmerdam – TNaaS – Tech Names as a Service

Ross Kippenbrock – Finding Lane Lines for Self Driving Cars

Rafael Schultze-Kraft – Building smart IoT applications with Python and Spark

Sirin Odrowski – Introduction to Search

Karolina Alexiou – Patterns for Collaboration between Data Scientists And Software Engineers

Raphael Pierzina – Kickstarting projects with Cookiecutter

Max Humber – Patsy: The Lingua Franca to and from R

Jo-fai Chow – Introduction to Machine Learning with H2O and Python

Alexandru Agachi – Introductory tutorial on data exploration and statistical models

Lightning Talks

Trent McConaghy – Blockchains for Artificial Intelligence