Applied Machine Learning Days

AMLD 2018


Artificial Intelligence



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Applied Machine Learning Days
January 27-30, 2018
EPFL, Lausanne, Switzerland
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The Applied Machine Learning Days will take place from January 27th to 30th, 2018, at the Swiss Tech Convention Center on EPFL campus. It is one of the largest machine learning & AI events in Europe, focused specifically on the applications of machine learning and AI, making it particularly interesting to industry and academia.
Saturday & Sunday will be hands-on, with workshops, trainings, a hackathon and tutorials. The main conference will take place on Monday and Tuesday, with a Networking Dinner taking place on Monday evening.
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Call for Posters
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We invite contributions of posters to the second event of the Applied Machine Learning Days which will consist in four exciting days of talks, discussions, posters and tutorials on Machine Learning and Artificial Intelligence.
We welcome all kinds of contributions related to machine learning and applications, including scientific, industrial or interdisciplinary projects by both students and professionals of all backgrounds. Demos and show-cases are highly welcome.
We plan to select a broad set of interesting applications which benefit discussion and interaction, and select the best posters for a short 3min spotlight plenary presentation.
SUBMISSION DEADLINE: November 28, midnight.
NOTIFICATION: December 1
Link: www.appliedmldays.org/call_for_posters
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SPEAKERS
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- Christopher Bishop (Microsoft)
- Joanna Bryson (Universities of Bath & Princeton)
- Soumith Chintala (Facebook)
- Raia Hadsell (DeepMind)
- Jeremiah Harmsen (Google)
- Frédéric Kaplan (EPFL)
- Amnon Shashua (Mobileye, Hebrew University of Jerusalem)
- Martin Vetterli (EPFL)
- Olga Russakovsky (Princeton University)
- CrowdAI winners
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Workshops
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Open Food Hackdays 2018 / Machine Learning for News: Theory, Applications and Visualisation in Python / Machine Learning with Go / The Data Ring: A canvas for Data Project / Discovering Brain Structure with Machine Learning / Financial Predictions with Machine Learning / Machine Learning Reproducibility at Scale using Open Source Tooling / Crash course in Deep Learning and PyTorch / Tensorflow Basics / Machine Learning meets Advanced Manufacturing and Materials Science / Spatial Data Science with Open Data and Social Media / Unsupervised Learning in Brain-Computer Interfaces: Theory and Practice / "Reatching" into the rabbithole: Should we replace politicians with algorithms? / Modeling timeseries and sequence data on AWS using Apache MXNet and Gluon / Applied Machine Learning for Anomaly Detection / Girlscoding: Teaching Computers How to Think / Artificial Intelligence for Artists