Wednesday, November 15, 2017

Ce soir: Paris Machine Learning #3 Season 5, PokémonGO, Unsupervised ML in high dimension,, Learning to program

Thanks to Cap Digital for hosting us. The streaming of the event is here:

The capacity of the room is about 100 seats. Audience comes as first-come-first-serve then door closes. Registration is here. Here is the tentative schedule : 

6:45PM doors open / 7-9:00PM talks / 9-10:00PM drinks/foods / 10:00PM end

and the presentations

Franck Bardol, Igor Carron, Welcome.

short presentation (1-2 minutes)

David Perlmutter, Implicity
IMPLICITY analyzes pacemaker data with machine learning and web semantics. We are hiring data scientists. You're welcome !
Longer presentations:

This talk uses Pokémon GO to illustrate how data science can help metagaming. In particular, it uses game theory (payoff matrix, minimax criterion), machine learning and operation research to solve practical problems in Pokémon GO. 

In this talk, i will present some work carried out during my PhD CIFRE at Artefact on unsupervised learning, clustering in high dimension on GMM and mixture density estimation. 

A working machine learning system involves a lot of moving parts, and the typical workflow, from data acquisition to industrialization is not well standardized and will usually take you through a variety of languages and frameworks. We introduce, a platform that allows to reduce this complexity by automatically managing model training and deployment, so that data scientists can focus on solving problems and creating value for customers. 

BeepAI, a multiplatform, "connected" artificial intelligence that learns to program. BeepAI is able to find the most efficient algorithm to solve logic, mathematics and other computer problems in a network of "connected virtual machines". Once a solution found BeepAI actually benefit the other instances of the network thanks to a library of "solutions" that is built up as the results are found.

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