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
PyData Berlin 2017
56 Talks