Paris Machine Learning Meetup Archives

Paris Machine Learning Meetups Archives

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All the slides and videos of the previous meetups can be found below:

Season 7 (September 2019 - June 2020)

TBD

Previously on the Paris Machine Learning meetup:

Season 6 (September 2018 - June 2019)

• Paris Machine Learning Meetup #6 Season 6, (Season 6 Finale). Data4Good, Hybridation, Sequence Predictions, Unreliable Data, June 12, 2019, Sponsor: Morning-coworking
• Emmanuel Bacry, CNRS, University Paris-Dauphine, Two ambitious AI for Good projects
• John Whitbeck, Liftoff, Reliable ML on unreliable data
• Nabil Benmerad, Arnaud de Moissac, DCBrain, ML/RL for Combinatorial Optimisation : the next big thing
• Gilles Madi, Prevision.io, Auto-Lag Networks for Real Valued Sequence to Sequence Prediction


• Hors Série #2 Season 6, Universal Adversarial Robustness, a free lunch ? Hosted by villettemakerz.com, May 28th, 2019
• Elvis Dohmatob, Criteo AI Lab, will present some new results on adversarial robustness in machine learning

• Paris Machine Learning Meetup #5 season 6: Fraud & Bank attacks, Robust Learning, Data Olympics Paris Berlin 2019, May 15th, 2019. Hosting provided by Morning coworking
• Antoine Thorin, BRED-Banque Populaire, From reactivity to proactivity in ATM maintenance
• Gael Varoquaux, INRIA, Learning with missing values
• Gregoire Martinon, Quantmetry, Real-time detection of attacks on ATMs
• Fabien Vauchelles, Zelros, Data Science Olympics 2019, Paris and Berlin

• Machine Learning #4 Season 6: Adversarial Attacks, ML in Production, XAI, Photo Editing, sponsored by Meero, April 10th, 2019
• Jean-François Goudou, Meero, AI in photo editing: what is reality ?
• Battista Biggio, Machine Learning Security
• Christophe Denis, Explainable and convivial AI tools for healthcare
• José Sanchez, Axionable, Machine Learning in production, the challenges to create value
• Paris Machine Learning #3 season 6: Explainable AI, Unity Challenge, Ethical AI, March 13th, 2019.
• introduction to CFM Capital, Eric Lebigot
• Vincent-Pierre Berges, The Obstacle Tower A Generalization Challenge in Vision, Control, and Planning, https://unity3d.com
• Manar Toumi, Leornardo Noleto, Interpretability, https://www.bleckwen.ai
• Cloderic Mars, craft.ai, Explainable AI
• Arnaud de Moissac, https://dcbrain.com, Impact AI

• The 100th Paris Machine Learning meetup tonight: La revanche des neurones, DL on Healthrecords, Search-Oriented Conversational AI, Nanotechnology and electricity consumption. Thanks for Scaleway for hosting us and provide catering ! February 13th, 2019.
• Introduction par les organisateurs Franck Bardol, Jacqueline Forien, Jean Latière, Igor Carron
• Dominique Cardon, (Medialab Sciences Po), La revanche des neurones ?
• Alice Mizrahi (UMPHY) Reduce the energy consumption of artificial intelligence through nanotechnology
• Fajwel Fogel, (Sancare), Deep Learning on health records, www.sancare.fr
• Wafa Aissa, Adrien Pouyet (UPMC Sorbonne Université), Search-Oriented Conversational AI, http://www.upmc.fr/en
• Bruno Lajoie (tmrow.com)www.tmrow.com

• Paris ML E#1 S#6: Conscience, Code Analysis, Can a machine learn like a child? November 14th, 2018, Hosted and sponsored by Samsung
• Presentation Gilles Mazars, AI Labs Samsung,
• Random Forest methods for robust prediction of diagnosis in disorders of consciousness, Denis Engemann, Inria
• Machine Learning on Source Code, Eiso Kant, sourced.tech
• Autonomous developmental learning: can a machine learn like a child? , Pierre Oudeyer

• Hors série #1 September 19, 2018 WiMLDS and Paris Machine Learning meetup Hors série #1: Scalable Automatic Machine Learning with H2O with Erin Ledell and Jo-Fai Chow.

Season 5 (September 2017 - July 2018)

• Paris Machine Learning #9, Season 5: Adaptive Learning, Emotion Recognition, Search, AI and High Energy Physics, How Kids code AI? June 14th, 2018, Hosting and sponsoring provided by SwissLife.
• Cecile Germain, TrackML Challenge, Can Machine Learning (ML) assist High Energy Physics (HEP) in discovering and characterizing new particles? Site: 
• Grégoire Martinon, Quantmetry, Concept drift and adaptive learning
• Andrei Petrovskii, Dreamquark.com, CNN+LSTM architecture for speech emotion recognition
• Patrick Elhage, Wypl : Reducing user reliance on search engines by fully exploiting internet navigation history
• Romain Liblau, Magic Makers, How teenagers code NeuralNets ? How can we teach teenagers to code AIs ? 


• Paris Machine Learning #8 Season 5, Chat with Self Driving Cars Engineers at Voyage, Funding AI at Zeroth, NVIDIA GTC, Quality inspection at Scortex and Low Rank Matrices. Thanks LightOn (http://lighton.io ) for hosting this meetup and thanks to DotAI (https://www.dotai.io/ ) for sponsoring the networking event. April 11th, 2018
• Chat with Self Driving Car engineers Tarin Ziyaee,Emrah Adamey, Nishanth Alapati , Tarek El-Gaaly of Voyage (https://voyage.auto/)
• Sami Moustachir, Data for Good - Annonce du projet d'un serment d'hippocrate pour les travailleurs de la donnée
• Guillaume Barat, NVIDIA, NVIDIA updates (https://www.nvidia.com/en-us/gtc/topics/deep-learning-and-ai/) - How to accelerate AI ?
• Pierre Gutierrez, Scortex.io (http://scortex.io), Automating quality visual inspection using deep learning
• Rodolfo Rosini , zeroth.ai (http://zeroth.ai/), we fund the stuff that nobody else will
• Wenjie Zheng, Learning Low-rank Matrices Distributedly without Factorization



• Paris Machine Learning Meetup #7 Season 5, Natural Language Understanding (NLU), AI for HR, decentralized AI hosted by Urban Linker !  March 14th, 2018.
• Joseph Dureau, Snips NLU (http://snips.ai), an Open Source, Private by Design alternative to cloud-based solutions
• Erik Mathiesen, Octavia.ai (https://octavia.ai), An AI Careers Advisor: Using Machine Learning to Predict Your Career Path
• Morgan Giraud, OpenMined (openmined.org)

• Paris Machine Learning Meetup #6 Season 5: Nano-neurons, Drug design, Electronic Health Records.... Hosting and sponsoring provided by Zenika. February 14th, 2018
• Event extract on Twitter, Serge Nakache
• Winning team Datathon MIT & APHP 2018, Marc Lelarge: Presentation
• Artificial intelligence for new drug design, Quentin Perron, iktos.ai 
• Artificial hardware nano-neurons, Julier Grollier , CNRS

• Hors série #8: Sprint scikit-learn development contribution
• Hors Série #7 Croix Rouge Datathon
• Hors Série #6 @ Devoxx
• Hors série #5: "Past and Present and Future of Deep Learning and Artificial Intelligence" Juergen Schmidhuber. Hosting and sponsoring of the event thanks to Meritis.
• Hors série #4 Saison 5: Le Canada et l'IA, hosting provided by Xebia and sponsoring by Ambassade du Canada à Paris. April 16, 2018
• “ How we solve Poker” by Prof. Mike Bowling, Computing Science, University of Alberta (15 minutes). 
• « L’AI at the University of Alberta » by Prof Mario A. Nascimento, Full Professor and Chair of the University of Alberta's Department of Computing Science. 
• Présentation de Element AI par Sarah Larose, Public Policy & Government Relations chez Element AI. Site: http://element.ai
• Deep Learning, Artificial Evolution and Novel AI Behaviors by Vadim Bulitko, Associate Professor at the University of Alberta, Department of Computing Science. Site: http://agi-lab.net
• Understanding the role of exploration in heuristic search by Martin Müller, Computing Science, University of Alberta. 
• "Natural Language Processing at the University of Alberta" by Prof. Greg Kondrak 
• Présentation des opportunités de bourses pour étudier et faire de la recherche au Canada par Fevronia Novac Chargée de programmes, Service Enseignement supérieur et Jeunesse, Ambassade du Canada en France
• Hors série #3, Machine Learning and Graph DB. Co-organized with eo4j and hosted by Criteo
• Vlasta Kus Graph-Powered Machine Learning
• Christophe Knowledge Graph
• Hors série #2, season 5, ML courses for developers, Hosted by simplon
• Hors série 1 (Season 5) Self-Supervised Imitation, Pierre Sermanet, January 4th, 2018. hosting and sponsoring of the event thanks to Meritis.

• Paris Machine Learning Meetup #5 Season 5: IronCar, Mobile AI, Rules mining. Thanks to Fortia for hosting this meetup and sponsoring the food and drinks afterwards. January 10th, 2018.

• Franck Bardol, Jacqueline Forien, Igor Carron, La newsletter de Janvier (presentation)
• Sebastien Treguer, AutoML 2018 competition
• Sibylle de Villeneuve, Présentation de la compétition IronCar
• Vincent Houlbrèque, IronCar, Présentation de la voiture Xbrain, video (Meetup IronCar France)
• Sira Ferradans, Rules mining : How to spot rules in a prospectus ? (video)
• Merouane Debbah, Mobile AI

• Paris Machine Learning meetup Hors Série 1 (Season 5) Self-Supervised Imitation, Pierre Sermanet January 4th 2018,  Meritis hosted and sponsored this meetup 
• Pierre Sermanet, Self-Supervised Imitation

• Paris Machine Learning Meetup #4 Season 5: K2, Datathon ICU, Scikit-Learn, Multimedia fusion, Private Machine Learning. Thanks to Invivoo for hosting and sponsoring this meetup. December 13, 2017.
• Pierre Saurel (Cercle K2), Trophees du Cercle K2, Short talk, Edition 2018. Call for candidates. It remains just few days to send your thesis, research paper, .https://www.cercle-k2.fr/trophees-k2...
• Adrien Parrot (Assistance Publique Hopitaux Paris), Datathon for intensive care event, short talk.
• Gael Varoquaux (INRIA), Some new and cool things in Scikit-Learn 
• Nhi Tran (Invivoo), Multimedia fusion for information retrieval and classification
• Morten Dahl, Private Machine Learning
• Hardware: The War for AI supremacy, Recent developments, a short review, Igor Carron, LightOn.io

• Paris Machine Learning #3 Season 5, PokémonGO, Unsupervised ML in high dimension, Prevision.io, Learning to program. Thank you to Cap Digital for hosting us. November 17th, 2017
• Franck Bardol, Igor Carron, Welcome.
• Wenjie Zheng, Pokémon GO, metagaming and data science (html version)
• Mehdi Sebbar, On unsupervised learning in high dimension, Optimal KL Aggregation in density mixtures estimation 
• Gerome Pistre, Prevision . io Prevision.io
• Cedric Vasseur, BeepAI, Artificial Intelligence that learns to program

• Paris Machine Learning Meetup #2 Season 5: Reinforcement Learning for Malware detection, Fraud detection, video scene detection and Climate Change. Hosted and sponsored by Artefact The video streaming is here. October 18th, 2017

• Philippe Rolet, (Artefact) , Short presentation of Artefact
• Hyrum Anderson, (Endgame), Reinforcement Learning for Evading Machine Learning Malware Detection
• Olivier Risser-Maroix, Detection of Opening Scenes in Series on video frames
• Olivier Corradi, Building a sustainable Tomorrow with data and AI (Tomorrow), electricitymap.org
• Robin Lespes, Gill Morisse, Romain De San Nicolas, Selection Bias in Fraud detection(Quantmetry)

• Paris Machine Learning #1 Season 5: Code Mining, Mangas, Drug Discovery, Open Law, RAMP, Video is here. Hosted and sponsored by #LaPiscine. September 14, 2017
• Franck Bardol, Igor Carron, We know what the AI world did Last Summer 
• Xavier Lagarrigue, Presentation de Deep Algo, La Piscine
• Laurent Cetinsoy, Challenge Datascience JNI - CDS - Pollinisateurs  (pdf) Lien : http://bee-o-diversity-challenge.strikingly.com/
• Open Law: IA et droit, Dataset d'apprentissage , Olivier Jeulin, Lefebvre-sarrut.eu http://openlaw.fr/travaux/communs-numeriques/ia-droit-datasets-dapprentissage
• Challenges in code mining, Information theoretic approach, Jérôme Forêt, Head of R&D de Deep Algo, Deep Algo 
• Using Posters to Recommend Anime and Mangas by Jill-Jênn Vie,  (livestream from Japan). Site: http://research.mangaki.frRelevant ArXiv: https://arxiv.org/abs/1709.01584
• Generative models for drug discovery. Application to crypto-currencies by Mostapha Benhenda (Startcrowd.club) 
• RAMP: a tool for data science workflow building and crowdsourcing by Balazs Kegl (also on slideshare).Links: https://www.ramp.studio https://github.com/paris-saclay-cds/ramp-workflow https://medium.com/@balazskegl

Season 4 (September 2016-June 2017)

• Paris Machine Learning #10 Ending Season 4 : Large-Scale Video Classification, Community Detection, Code Mining, Maps, Load Monitoring and Cognitive (video is here). hosted and sponsored by SCOR . June 27, 2017.
• Antoine Miech (Doctorant à l'INRIA, Gagnant du Kaggle YouTube 8M challenge ) , Learnable pooling methods for large-scale video classification
•  Christel Beltran (IBM) Innovating, Differenciating with Cognitive
• Aurélia Negre, Adèle Guillet - Quantmetry, Théorie des graphes : détection de communautés et cas d'application (demo link)
• Juliette Tisseyre - Code Case Software, Code Mining vs Text Mining
• Remi Dellasus - Qucit, Mapping Learning, How to use machine learning to extract maps data from satellite images ?
• Simon Henriet - Smart Impulse, Non Intrusive Load Monitoring

• Paris Machine Learning meetup "Hors Série" #13: Automatic Machine Learning. Hosted by Sewan . June 13th, 2017
• Axel de Romblay: MLBox
• Alexis Bondu. Edge ML
• Slides French version (link), English version (link)

• Paris Machine Learning Hors Série #12: Deep Learning and Classification of Pollinating Insects May 29, 2017. Organised by Saclay CDS. Hosted at La Paillasse and sponsored by NVIDIA. Video is here.
• Paris Machine Learning Meetup #9 Season 4 @ IHP, Bias, Ethics & Fair Algorithms. May 11, 2017. The meetup was hosted by l'IHP, and sponsored by Quantmetry.
• Cedric Villani
• Dominique Cardon
• Pierre Saurel
• Franck Bardol
• Igor Carron

• Paris Machine Learning Hors Serie #11 : Workshop SPARK (atelier 2)
• Leonardo Noleto, slides et les TPs corrigés: 

• Paris Machine Learning @ Devoxx April 6, 2017· 

• Paris Machine Learning Hors Serie #10 Season 4 Workshop SPARK (atelier 1) March 22, 2017
• Leonardo Noleto, slides et les TPs corrigés: 

• Paris Machine Learning #7 Season 4 @ Algolia, deep reinforcement learning, feature engineering. Hosted and sponsored by Algolia, March 8th, 2017
• Zineb Lamrani, Set-up start tech and Fashion 
• Katja Hofmann, Project Malmo: Turning Minecraft into a testbed for the future of AI, Microsoft Research Cambridge. Site: Project Malmo Project Malmo video: https://youtu.be/KkVj_ddseO8, Video link
• Thomas Seleck, Features engineering by a Kaggle Expert, HTML link
• Jean-Baptiste Priez, Données relationnelles et Feature Engineering, Predicsis
• Edmund Ronald, Get your paper accepted
• Yaël Frégier, Internship offer ( https://sites.google.com/site/homepagelml/ )

• Paris Machine Learning Hors Série #8 Season 4: Azure Machine Learning (video) ( The event was sponsored by Microsoft and hosted by Cellenza.March 1st, 2017.
• Introduction à Microsoft Azure par Cellenza, Marius Zaharia
• Microsoft Azure Machine Learning, Benjamin Guinebertière
• Compte gratuit: http://studio.azureml.net, Github: aka.ms/azureml170301

• Paris Machine Learning, Hors Série #7 Season 4: Machine Learning for Arts, Gene Kogan The event will be hosted and sponsored by our good friends at Mobiskill. February 27th, 2017 
• Gene Kogan, video of the 1h30min talk is here. Github, ML4A (Machine Learning for Artists)

• Paris Machine Learning #6 Season 4, Symbolic AI, Recommendations & Naïve Bayes (video)  The event was hosted and sponsored by Sociéte Générale. February 21st, 2017.
• Franck Bardol, Igor Carron, What's happening
• Fabrice Popineau, Symbolic computation : where does it fit in today's Artificial Intelligence ?
• Mehdi Sakji, Développement d'un système de recommandation de contenu
• Sylvain Ferrandiz, Soyons naïfs, mais pas idiots

• Paris Machine Learning Meetup #5 Season 4: LIME 'Why should I trust you', Apache SAMOA, GAN for Cracks, Opps . Hosted and sponsored by Mobiskill. Video is here. January 11th, 2017

• Marco Tulio Ribeiro, "Why Should I Trust You?" Explaining the Predictions of Any Classifier ", [code]arxiv link (short video presentation and longer KDD presentation)
• Albert Bifet, Telecom-Paristech, "Apache SAMOA" Github repo
• Julien Launay "Cracking Crack Mechanics: Using GANs to replicate and learn more about fracture patterns" without animation link is here
• Daniel Benoilid, foulefactory.com, 5 min talk "Man + Machine : Crowdsourcing opportunities"

• Paris Machine Learning #4 Season 4: ML for Ultrasound, export/import in R, electric consumption from times series, #NIPS2016 Hosted by Dojo and sponsored by Snips.ai. December 21, 2016. 

• Anne-Laure Rousseau, Machine Learning appliqué aux images d'échographie,
• Mathieu Lechine, Benchmark: Import/export functions in R
• Jiali Mei, Reconstituer la consommation d'électricité à partir des agrégations temporelles / Recovering Multiple Nonnegative Time Series From a Few Temporal Aggregates,
• Igor Carron, So What happened at NIPS 2016 ?

• Paris Machine Learning, Hors série #5 Season 4: : See.4C Spatio-temporal Series Hackathon hosted at La Paillasse and sponsored by NVIDIA. February 14th, 2017



• Isabelle Guyon (UPSud Paris-Saclay). Welcome.
• Florin Popescu (Fraunhofer). Project presentation. Sneak peek at the data.
• Sergio Escalera, Xavier Baro, and Julio Jacques (U. Barcelona). Sneak peek at the data and deep learning benchmarks.
• Gael Varoquaux (INRIA). Learning on very noisy spatio-temporal series: lessons from fMRI.
• Ivan Laptev (INRIA). Video forecasting, a computer vision approach.
• Sebastien Treguer (La Paillasse). Hands-on Python tutorial for spatio-temporal time series prediction.
• Antoine Marot (RTE).Forecasting power flows to ensure grid security with increasing complexity.
• Nadine Peyrieras (CNRS). Analyzing 3D+time microscopy images of developing organisms and reconstruct their cell lineage.
• Balazs Kegl (CNRS, Paris-Saclay Center for Data Science).  Data challenges with modularization and code submission: Lessons learned
• Stephane Ayache and Cecile Capponi (AMU). Announcement of the winners of the day and wrap up.
• Paris Machine Learning Hors Série #4 Season 4 on Deeplearning4j: Applying Deep Learning to business problems in production. Hosted by Cap Digital . Video is here. Nov 28, 2016
• Chris Nicholson. Deeplearning4j: Applying Deep Learning to business problems in production

• Paris Machine Learning Meetup #3 Season 4: OPECST, Correlations, Transfer Learning, DL @Amazon, Car Sales (video)  Nov 8, 2016, Hosted by AAA Data and sponsored by zen.ly

• Dominique Gillot, Sénatrice, ancienne ministre et Rapporteure avec le député Claude de Ganay d'un rapport sur l'Intelligence Artificielle pour le Parlement. Mehdi Benhabri, Administrateur de l'Office parlementaire d'évaluation des choix scientifiques et technologiques (OPECST). Important: Pour celles et ceux qui ne pourraient pas parler à la sénatrice, un questionnaire en ligne est disponible et les réponses seront adressées aux deux rapporteurs et à l'administrateur qui suit le dossier IA.  Slide

• Pierre Leveau, Presentation of zen.ly

• Franck Bardol, Deviens un Data Ninja

• Julien Simon (Amazon), Machine Learning and Deep Learning with Amazon Web Services

• Gautier Marti (Hellebore Capital), A closer look at correlations
• Yannis Ghazouani (Dataïku), Labelling images using transfer learning. An application to recommender systems
• Sébastien Claude (AAA Data), Qui va vendre son véhicule dans les 3 prochains mois ?

• Paris Machine Learning, Hors Série #3: Mathématiques et Data Science( video) October 20, 2016
• Roundtable around Mathematics and Data Science. Organized by Quantmetry

• Cédric Villani, Vincent Lefieux, Philippe Azoulay, Mathilde Mougeot, Julie Josse, Nicolas Le Roux 

 
• Paris Machine Learning Meetup #2 Season 4: Emotional AI, Regaind, Health Knowledge....  October 12th, 2016 . Meetup hosted and sponsored by Vente-privée.

• Franck Bardol, Igor Carron, intro  (newsletter October 2016)
• Clementine Delphin, Startup Weekend Artificial Intelligence (slides pdf version, 2 mins)
• Olivier Dolle, Ivan Vukic, Antoine Deblonde Vente-privée, Machine Learning at Vente-Privée, 
• Frederico Quintao, Health Knowledge Framework for the Web.
• Gregory Renard, Xbrain,  "Emotional AI", http://www.people2vec.org/
• Arnaud Laurenty, Regaind.io What I wish a ninja data scientist had told me before we started building our datasets :)
• Debate with François Némo, "Pourquoi l'Europe est-elle absente de la guerre des plateformes ?" (pdf version) 

• Paris Machine Learning Meetup, Hors Série #2, Season 4: Scalable Machine Learning with H2O, September 21st, 2016, Hosted and sponsored by Murex.

• Jo-fai Chow and Jakub Háva. All the presentations are at: http://bit.ly/h2o_paris_1

• Paris Machine Learning Meetup, Hors série #1, Season 4: Introduction to Bayesian Inference with Stan and R September 20th, 2016, Hosted and sponsored by Dataiku. Video is here.
• Eric Novik, Introduction to Bayesian Inference with Stan and R
• Eric Kramer, DSS + Stan
• Panel with Eric Novik, Daniel Lee and Michael Betancourt

• Paris Machine Learning #1 Season 4: AlphaGo, Deep Learning & Global Biodiversity, DataScience Game, September 14th, 2016, Hosted by DojoCrea. Video is here.

• Franck Bardol and Igor Carron. Introduction
• Louis Dorard, Le Machine Learning Canvas
• Antoine Dusséaux, le Paris NLP meetup
• David Klein - 'Deep Learning for Global Biodiversity Monitoring'
• Stephane Senecal, Les techniques de Machine Learning au cœur du succès d’AlphaGo
• Antoine Ly + équipes UPMC & Polytechnique, DataScience Game , Presentation slides., Presentation slides UPMC

Season 3 (September 2015- June 2016)

• Paris Machine Learning Meetup #14 Season 3: Dare Mighty Things. June 8th, 2016, The meetup was hosted and sponsored by Lagardere.The video is here.

• Olivier Sorba, Lagardere, presentation
• Rony Rozas, LeGuide Lagardere, presentation2
• Lenka Zdeborova, ( @zdeborova ) Institut de Physique Theorique de Saclay/CNRS, What can physics bring to machine learning?
• Mark Reidl, (@mark_riedl ),Georgia Tech, Using Stories to Teach Human Values to Artificial Agents
• Antoine Bordes Facebook AI Research lab (FAIR), Memory Networks pour questions-réponses et systèmes de dialogues
• Raymond Francis (@CosmicRaymond) NASA/JPL, AEGIS Autonomous Targeting Software Deployed for the Curiosity Rover's ChemCam Instrument (ppt version)
• Philippe Duhamel,(@teamkadeal ) Clustaar, Moteur de conversation pour les bots/robots (ppt)
• Stephane Bura, (@s_bura ) Weave.ai, Machine Learning and OS (ppt ) (see also AI and the future of the Operating System)
  
• Robert Salita, Comparing, Benchmarking AI Services

• Paris Machine Learning Meetup #13 Season 3; Data augmentation methods in Deep Learning, Neural Turing Machines, Manga Recommendation. May 11, 2016 We will be hosted by DojoEvents. Refreshements and food will be offered by dotAI.io. Video is here.
• Meilleur Data Scientist de France (epilogue d'hier), Jeremy Atia , Romain Ayres , Christophe Bourguignat, Stéphane Fenia, Fabien Vauchelles
• Vinay Kumar (arya.ai) Role of Data augmentation methods in Deep Learning
• Tristan Deleu (snips.ai) Learning Algorithms (and more) with Neural Turing Machines
• Jill-Jênn Vie (mangaki.fr) Mangaki, recommandation de mangas open source
• LightOn, Igor Carron, CEO, presentation
• Franck Bardol, Igor Carron, Introduction
• Blue Frog Robotics, Jean-Michel Mourier, CTO, presentation 

• Paris Machine Learning Meetup #12 Season 3: ML Hardware April 26, 2016.Video is here. Mozilla France is hosting us. Mathworks is sponsoring the networking event afterwards. Video is here.
• Arjun Bansal, Nervana Systems, Nervana and the Future of Computing
• Marc Wolff, Amine El Helou , Mathworks, Un algorithme distribué de forêt aléatoire pour du risque de crédit/ A Random Forest distributed algorithm for credit risk computations.
• Olivier Guillaune, Any computer for my machine learning?
• Igor Carron, LightOn.io, Approximating kernels at the speed of light
• André Reinald, Mozilla, Projet peerstorage.org

• Paris Machine Learning #11 Season3: Rogue Waves, Dataiku, eLum, Human Resources, DNN on Matlab, Data and Cars, The Great Convergence. April 13, 2016.   Xebia hosted us and sponsored the event.Video is here.

• Matthieu Blanc, Yoann Benoit, Presentation Xebia
• Paul-Henri Hincelin, Dataiku, Putting Data science in productio
• Martin Prillard, Talentoday, How psychometrics and machine learning can identify corporate cultures and business success factors
• Amine El Helou, Deep Learning in Matlab.
• Igor Carron, "The Great Convergence" or How ML/DL is disrupting sensor design.
• Themis Sapsis (MIT) Robust prediction of extreme wave events in realistic seas
• Laurence Vachon  Mission On Mars Robot Challenge 2016
• Florent Pignal, drust.io Drust: Application de la data science à des données du véhicule connecté!
• Cyril Colin, Karim Elalami , eLum, Artificial Intelligence Driven Energy Management, Elum Energy - Load forecasting for nano-grid management

• Paris Machine Learning Meetup #10 Season 3, M&A, Speech Recognition, Telecoms, Mariana DL & HR, March 9, 2016. Meetup was hosted and sponsored by Capgemini Consulting. Video is here.
• Franck Bardol, Igor Carron, Introduction
• Olivier Auliard et Charlotte Delaunay (Capgemini Consulting) Mergers and Acquisitions  
• Gabriel Synnaeve (FAIR) Summary of his talk @ Collège de France : Reconnaissance de la parole / Speech recognition Slides
• Stéphane Senecal (Orange) Reinforcement Learning for Resource Allocation in Wireless Communication Networks
• Tariq Daouda (University of Montreal) Mariana, the Cutest Deep Learning Framework.. Talk around Mariana Deep Learning Library (Tariq vient de créer un dossier publique qui contient ses présentations sur Mariana, il y a inclut les slides de la présentation du meetup ainsi que l'exemple de live coding.https://drive.google.com/folderview?id=0B5hz7qLUGDHOLTQzSl81aktMYVE&usp=sharing)

Paris Machine Learning Newsletter, March 2016 [In French]

• Paris Machine Learning Meetup #9 Season 3: Machine Learnings in Music / A First step with TensorFlow , February 22, 2016. Hosted and sponsored by Mobiskill @Mobiskill.  Video is here.
• Inès Al Ardah, présentation de Mobiskill 
• Bob Sturm, " Your machine learnings may not be learning what you think they are learning: Lessons in music and experimental design" @boblsturm ( The Endless Traditional Music Session,  Deep learning for assisting music composition (parts 1-4) Deep Christmas Carols)
• Jiqiong Qiu "First step Deep Learning with Tensorflow" site associé: https://github.com/Sfeir/demo-tensorflow
• Pitch: David Marie-Joseph, "La reconnaissance visuelle au service du shopping"

• Paris Machine Learning Meetup #8 Season 3: Fair and Ethical Algorithms, February 17, 2016, hosted by Dojocrea (@Dojocrea ) and sponsored by. ELS - Editions Lefebvre Sarrut, It Lab (@elsitlab ). Video is here.

• Franck Bardol and Igor Carron, Introduction
• Suresh Venkatasubramanian, An Axiomatic treatment of fairness @geomblog Here is an Al Jazzera show on the general subject of fair algorithms (with Suresh in it).
• Michael Benesty Application of advanced NLP techniques to French legal decisions ​​ (pdf) @pommedeterre33 Attendant website: http://www.supralegem.fr
• Michel Blancard, CMAP / EtaLab, A sunny day in the CDO team of the French gov , @Etalab and @OpenSolarMap. Attendant website: OpenSolarMap
• Pierre Saurel, "Ethics of Algorithms: still an oxymoron?"

• Paris Machine Learning Meetup #7 Season 3: Neural Networks for Predictive Maintenance, Machine Learning in Quantitative Finance, Introduction to scikit-learn , February 11, 2016,
  hosted by Quantmetry and sponsored by Quantopian. Video is here.

• Pitch: Olivier de Fresnoye, RAMP 9-10 Février, http://www.epidemium.cc.  
• Pitch: Gautier Marti, Pitch Cleared Derivatives Solutions  
• Franck Bardol and Igor Carron, introduction  
• Heloise Nonne, Quantmetry, Réseaux de neurones pour la maintenance prédictive  
• Delaney Granizo-Mackenzie, Quantopian, "Machine Learning in Quantitative Finance"
• Olivier Grisel, Introduction to scikit-learnand what's new in v0.17

• Paris Machine Learning Meetup #6 Season 3, February 10th 2015, ASTEC #NecMergitur, Beauty and danger of matrix completion, E-commerce and DL, Topic Modeling on Twitter streams and Cross-Lingual Systems. Maltem Consulting Group hosted and sponsored the networking event after the présentations. Video is here.

• Pitch; Manga Zossou, Projet AZTEC : Audio sensors for threat detection/système de capteurs audio pour détecter des menaces) at 11 minutes in the video. (in French)
• Franck Bardol and Igor Carron, introduction.  
• Julie Josse, Beauty and danger of matrix completion methods: unveiling a black box's subtleties for better decision at 1h14 in the video. (in French) 
• Andrei Yigal Lopatenko, Head of Search Quality @ WalmartLabs,  (remote from SF) What problems of ecommerce can deep learning solve? at 52 minutes in the video. (in English)
  
• Alex Perrier, (remote from Boston) at 24 minutes in the video. (in French) Topic modeling avec LSA, LDA et STM appliqué aux streams de followers Twitter.
• Jean-Marc Marty, Proxem, The Quest for Cross-Lingual Systems  at 1h57 in the video. (in French)

• Paris Machine Learning #5 Season 3, January 13th, 2015. Maltem Consulting Group hosted and sponsored the networking event after the présentations. Video is here. 
• Igor Carron, introduction 
• Franck Bardol, Intro to ML  
• Amine El Helou, The Mathworks (short)  
• Chloe-Agathe Azencott, Large p, small n: Feature selection with few samples in high dimension.  
• Sebastien Treguer,  AutoML Challenge,  
• Aurore Li, Composing Music with LSTM, Github  
• Kiran Varanasi, Modeling Human Faces. Some videos presented at the meetup can be found here.
• Summary:  Bach, Leonardo, Cancer and the Search for the Master Algorithm. A short summary of last night's Paris Machine Learning Meetup (#5 Season 3)

• Paris Machine Learning #4 Season 3, December 9th, 2015. Meetup was hosted by Société Française de Statistique. The networking event is sponsored by MathWorks. Video is here.
  
• Franck Bardol and Igor Carron, Introduction
• short announcement: Louis Dorard, PAPIs io
• Amine El Helou, Laurence Vachon, Machine Learning for IoT analytics 
• Julie Josse et Gérard Biau Presentation of Société Française de Statistique(in French)
• Yves Raimond,  Machine Learning at Netflix (in French)
• Matthieu Boussard, craft.ai (in French) Learning and behavior trees
• Jennifer Listgarten  Microsoft Research, In Silico predictive modeling of CRISPR/cas9 guiding efficiency. 

• Paris Machine Learning #3 Season 3, November 18th, 2015, Meetup at hosted and sponsored by Criteo . Video of the event is here.
• Franck Bardol, Igor Carron, Meetup introduction
• Nicolas Le Roux, Criteo presentation
• Emmanuel Dupoux, ENS Ulm, A report on the Zero Resource Speech Challenge (INTERSPEECH 2015)
• Olivier de Fresnoye, Epidemium (presentation slides), epidemium.cc
• Rebiha Rahba, Identification des ""Trending Topics"" ou comment utiliser le Machine Learning pour identifier les sujets qui font l'actualité ?
• Félix Revert, Du Machine Learning dans l'éducation des langues étrangères ?

•  Paris Machine Learning #2 Season 3, October 18th, 2015 (video is here)
• The meetup took place at SNIPS and was sponsored by Mathworks.
• Franck Bardol, Igor Carron,

• Raphael Puget, LIP6, starts a 14 mins 31 s Extreme multi-class classification with large number of categories / Classification multi-class dans un très grand nombre de catégories.
• R , information security , large protocol inspection and state machine analysis, Imad Soltani,
• "RTB à la Quant", JFT
• Mouhidine Seiv, Riminder.net
• Amine El Helou,  Mathworks, Upcoming webinar Machine Learning for Sensor Data Analytics,

• Paris Machine Learning Meetup #1 Season 3, September 9th, 2015, Snips hosted and sponsored this event. Video is here.

• Igor Carron, Franck Bardol, "September, when did that happen ?" 
• Samim Winiger, Roelof Pieters , Tales from a deeply generative summer: The coming of age of Creative AI
• Rand Hindi, Snips
• Maxime Pico, Startup42,  slides.
• Christophe Bourguignat, www.frenchdata.fr
• Amine El Helou, Mathworks,  slides , Kaggle's Right Whale competition

Season 2 (Sept 2014 - July 2015)

• Paris Machine Learning Meetup #10 Season 2 Finale: "And so it begins": Deep Learning, Recovering Robots, Vowpal and Hadoop, Predicsis, Matlab, Bayesian test, Experiments on #ComputationalComedy & A.I. June 17th, 2015. Mathworks hosted and sponsored this event. The video is here.
  

• Franck Bardol, Igor Carron, Meetup Presentation 
• Olivier Corradi,  Snips.net Lightning talk (at 7 minutes and 19 seconds in the video) Presentation slides 
• Heloise Nonne, Quantmetry, "Online learning, Vowpal Wabbit and Hadoop" (talk given in French at 12 minutes and 19 seconds in the video)
• Amine El Helou, Laurence Vachon, MathWorks. “MATLAB for Data Science and Machine Learning” (talk given in French at 35 minutes and 50 seconds in the video)  
• Samim Winiger, "Experiments on #ComputationalComedy and A.I." (remote from Berlin and in English , starts at 58 minutes and 22 seconds in the video)
• Ruslan Salakhutdinov, University of Toronto, Learning Multimodal Deep Models  (remote from Toronto and in English, starts at 1 hour 06 minutes and 08 seconds in the video)
• Florence Benezit-Gajic, PredicSis, "PredicSis: Prediction API" (talk given in English, starts at 1 hour 47 minutes and 55 seconds in the video)  
• Jean-Baptiste Mouret, INRIA/UPMC, "Robots that can recover from damage in minutes" . Attendant video:  https://youtu.be/T-c17RKh3uE - (talk given in French, starts at 2 hours 06 minutes and 40 seconds in the video) 
• Christian Robert Paris Dauphine, "Testing as estimation: the demise of the Bayes factors" (talk given in French, starts at 2 hours 26 minutes and 00 seconds in the video) 

• Paris Machine Learning Meetup #9, Season 2: ML @Quora and @Airbus and in HFT, Tax, APIs war. May 13th, 2015. AXA Data Innovation Lab hosted and sponsored this event. The video is here.
  

• Alberto Bietti, Quora,  Machine learning applications for growing the world’s knowledge at Quora
• Michael Benesty, TAJ, ML use case for French tax audit, Préparation d'un contrôle fiscal en France par l'utilisation du gradient boosting sur une comptabilité 
• Louis Dorard, Machine Learning APIs War: Amazon vs Google vs BigML vs PredicSis, Related blog entry.
•  Gerard Dupont, Airbus Defense and Space, Unstructured data processing – why ? How ?  Practical machine learning for intelligence applications
• Joaquin Fernandez-Tapia "High-Frequency Trading and On-Line Learning"
• Christophe Bourgignat, AXA, remise les prix du dernier concours DataScience.net AXA
• Igor Carron, Franck Bardol, Paris Machine Learning: Where Are We ?

 
• Paris Machine Learning "Hors Série" #3 (Season 2): AutoML Challenge Hackaton, April 23rd, 2015. The meetup was hosted and sponsored by ESPCI.

• Isabelle Guyon and Lukasz Romaszko (ChaLearn): Presentation of the AutoML challenge. Tips to solve it and win! 
• Olivier Grisel (INRIA): How to use Scikit-Learn to solve machine learning problems. 
• iPython notebook example given in talk
• iPython notebook to solve round 1of the AutoML challenge
• Julien Demouth (NVIDIA): Deep Neural Networks and GPUs.

• Paris Machine Learning Meetup #8, Season 2: Deep Learning and more... April 15th, 2015. The meetup was hosted and sponsored by Criteo. Video is here. The meetup was in conjunction with Deep Learning Paris meetup,  Kiev Deep Learning Meetup, London Machine Learning Meetup. 

• Presentation of Criteo by Damien Lefortier
• Yoshua Bengio, Title: Deep Learning Theory by Yoshua Bengio, 
• Classifying plankton with deep neural networks by the Deep Sea team from Reservoir Lab
  ( ppt version ) Sander Dieleman and Ira Korshunova, Ghent University  
• Learning to build representations from partial information: Application to cold-start recommendation by Gabriella Contardo, LIP6, UPMC 
• Sentiment Analysis With Recursive Neural Tensor Network / Analyse de sentiment à l'aide de réseaux de neurones récursifs Guillaume Wenzek

• Paris Machine Learning #7 Season 2: Fair Algorithms, The Automatic Statistician, ML & Entreprise. The meetup was hosted at Ecole 42. Video is here.

• Machine Learning et Entreprise , Francois-Xavier Rousselot ( Video at  1h16m )
• The Automatic Statistician, Zoubin Ghahramani, Cambridge University, site: The Automatic Statistician ( Video at 30m50s)
• Certifying and removing Disparate Impact, Suresh Venkatasubramanian,University of Utah and Sorelle Friedler, Haverford College, Site: Computational Fairness  ( Video at 5m28s)

• Paris Machine Learning #6 Season 2: Vowpal Wabbit, RL, Inmoov, libFM and more....The meetup was hosted by Ecole 42. The video is here.

• Franck Bardol, Igor Carron, Qu'est ce que le Machine Learning ( 0 to 11 minutes in the video)
• John Langford, Microsoft Research NY, "Vowpal Wabbit" Tutorial presentation slides ( 33:31 to 1:12:45 in the video, in English)
• Ludovic Denoyer, LIP6, Reinforcement Learning for Data Processing and Deep Reinforcement Learning ( 2:10:50 to 2:41:52 minutes in the video)
• Gael Langevin, "Can Inmoov be enhanced with Machine Learning ?", www.inmoov.fr/ ( 11 to 24 minutes in the video, Questions 1:13:00 to 1:24:45)
• Thierry Silbermann, University of Konstanz, libFM & Factorization Machines ( 1:24:45 to 2:10:42 minutes in the video)

• Paris Machine Learning Meetup "Hors Série" 2 (Season 2) Data Science for Non-Profits. The meetup was held at TheAssets.co. The video of the meetup is here (in French). A summary of the meetup is at: Sunday Morning Insight: The Hardest Challenges We Should be Unwilling to Postpone

• Isabelle Guyon, AutoML Challenge presentation (ppt), (pdf), ChaLearn Automatic Machine Learning Challenge (AutoML), Fully Automatic Machine Learning without ANY human intervention. ( short version pdf)
• Paul Duan, BayesImpact pdf presentation, http://www.bayesimpact.org
• Frederic le Manach, http://www.bloomassociation.org, On Subsidizing overfishing pdf, (ppt)
• Jean-Philippe Encausse, S.A.R.A.H,  presentation pdf, (ppt)
• Emmanuel Dupoux, ENS/LPS, The Zero Resource Speech Challenge (presentation pdf, presentation ppt )

• Paris Machine Learning #5 Season 2, Time Series and FinTech, Adversarial Algos: January 14th, 2015. Meetup held and sponsored by the Maltem Consulting Group. Video of the meetup is here (French).
• Presentation of the sponsor: the Maltem Consulting Group
• Anaël Bonneton (Agence Nationale de la Sécurité des systèmes d'information) Botnet detection with time series decision trees.
• Yves Lempérière (Capital Fund Management) "200 years of trend following"
• Gautier Marti (Hellebore Capital), "How to cluster random walks? - Application to the Credit Default Swap market" 
• James Nacass  API de trading  www.bigdtrade.com

• Paris Machine Learning #4 Season 2, Tips and advices for machine learning challenges, Biochemical Probabilistic Computation, Bias-variance decomposition in Random Forests and more. December 9th, 2014. Meetup sponsored by ANEO (http://aneo.eu/) and held at DojoCrea. Video in French here.
• Gilles Louppe, Bias-variance decomposition in Random Forests 
• Arnaud de Myttenaere, Tips and advices for machine learning challenges
• Pierre Bessière, Biochemical Probabilistic Computation 
• Jean-Pierre Malle , Situationnal Analysis Processors

• Paris Machine Learning #3 Season 2: Building a Data Science Team, Opinion Mining, Word2Vec, Kaggle . November 11th, 2014. Event sponsored by ANEO (http://aneo.eu/) and held at DojoCrea. Video in French here (Parisson) or here (Google Hangout).

• Romain Ayres (UPMC), Eric Biernat (OCTO) and Matthieu Scordia (Dataiku): Tradeshift Kaggle Challenge. Code for the online learning model and the model stack.
• Christophe Bourguignat, Building a Data Science Team (other)
• Vincent Guigue (UPMC-LIP6) Tutorial Opinion Mining
• Charles Ollion (Heuritech) Tutorial on vector representation of words (Word2Vec, GloVe)

• Paris Machine Learning #2 Season 2: : Learning Causality, Words, the Higgs & more. October 15th, 2014. Event sponsored by ANEO and held at DojoCrea. Video is here.
  

• David Lopez-Paz, "Learning to learn causality" remote from Germany.
• Emanuela Boros, "Learning word representations for event extraction from text"
• Balazs Kegl: “Learning to discover: machine learning in high-energy physics and the HiggsML challenge"
• Cédric Coussinet, Une demo de Nomoseed. http://www.nomoseed.com
• Franck Bardol, Donner un sens aux donnees des ONGs.
• Olivier Roberdet: Prizm, The First Learning Music Player (Kickstarter)
  

• Hors-Série:#1 Data Journalisme; ,Tuesday, September 30th, 201. Held at NUMA and sponsored by HopWork.  Video of the Meetup
• Chris Wiggins, Chief Data Scientist au New York Times, What is a computational biologist doing at the New York Times? (and what can academia do for a 163-year old company?)
• Nicolas Sauret, chef de projet médias à l'IRI (Centre Pompidou) et Bertrand Delezoide, Multimedia Research Team Leader (CEA-LVIC). Periplus: Articuler éditorialisation algorithmique et humaine
• Claude de Loupy, co-fondateur de Syllabs, Analyse sémantique & création de contenus textuels.
• Chrystèle Bazin, Des robots et des journalistes: Les mutations de l’information à l’heure du big data
• François-Xavier Fringant, co-fondateur de Dataveyes, spécialisée dans les interactions Hommes-Données,  
• #1 Paris Machine Learning #1 Season 2, A new Beginning: Snips, Nomo, Clustaar and more... (September 17th, 2014 at DojoCrea).Video of the meetup.

• Franck Bardol, Igor Carron, Introduction, What's New....
• Lightning talk: Jean-Baptiste Tien, Criteo, Update on the Kaggle Criteo contest  
• Maël Primet, Snips, Machine Learning for Context-Awareness
• Cédric Coussinet, http://nomoseed.org, Langage Nomo
• Philippe Duhamel & Nicolas Chollet (www.clustaar.com) , Extract Consumer Insight from Seach Engine Queries

Season 1 (June 2013 - July 2014)

•  Epilogue Season 1 (July 2014 at DojoCrea)

• #12: Paris Machine Learning Meetup #12: Season 1 Finale (June 16th held and sponsored at Google Paris). The remote presentation by Andrew Ng was synchronized with Zurich, Berlin and London.
• Europe Wide Machine Learning Meetup and Paris Machine Learning #12: Season 1 Finale, Andrew Ng and More...
• Saturday Morning Video: Europe Wide Machine Learning Meetup: Andrew Ng and more....
• Program:
• Francois Sterin
• Bastien Legras
• Andrew Ng, Baidu Chief Scientist, Coursera Chairman, Stanford (remote)
• S. Muthu MuthuKrishnan, Rutgers (remote) : Data Stream Algorithms: Developments and Implications for ML
• Yaroslav Bulatov, Google SF (remote), Multi-digit Number Recognition for Street View Imagery using Deep Convolutional Neural Networks
• Camille Couprie, IFPEN, Semantic scene labeling using feature learning
• videos: 
• Video 1
• Video 2
• Video 3
• Video 4
• Sam Bessalah, Stream Mining via Abstract Algebra (ppt version)

• #11: Paris Machine Learning Meetup #11 SPARFA Learning Analytics, Learning to Interact and more.. (May 14th, 2014. Held at and sponsored by Criteo)
• Louis Dorard , Les APIs de prediction
• Andrew Lan (SPARFA, Rice University) SPARFA: Sparse Factor Analysis for Learning and Content Analytics.
• Leon Bottou (Microsoft Research, ML group ) Learning to Interact 
• Maxime Oquab (INRIA) http://www.di.ens.fr/willow/research/cnn/ Object and action recognition with Convolutional Neural Networks. 


• #10: Paris Machine Learning Meetup #10: Dolphin Communications, Big Data, ConvNets and Quantum Computers (April 9th, 2014. Held at TheFamily and sponsored by HopWork)
• Brenda McCowan, Unraveling Dolphin Communication Complexity, Past approaches and next steps (YouTube video is here)
• Philippe Nieuwbourg, How to transform data into dollars… 
• Gabriel Synnaeve, Convolutional Neural Networks 101 
• Guillaume Palacios,  The D-Wave “Quantum Computer” Myths and Realities   
• Summary and video of the meetup
• Hors Serie: Paris Machine Learning Specialist Talk: Pierre Sermanet on OverFeat, Deep ConvNets (April 3rd, 2014. Held at Normale Sup)
• Deep ConvNets; "Astounding" baseline for vision, Pierre Sermanet
• #9 Paris Machine Learning Meetup #9: GraphLab, LocalSolver, Import.io and Matrix Factorization and Machine Learning (March 12th, 2014, held at and sponsored by DojoCrea)
• Lightning talk: 
• Allstate challenge @Kaggle, Kenji Lefevre
• The presentations:
• GaphLab, Unleash Data Science (ppt), Danny Bickson, GraphLab (remote presentation from Israel) 
• LocalSolver : A New Kind of Math Programming Solver, Julien Darlay
• Import.io  (ppt), Laurent Revel , Import.io,
• Advanced Matrix Factorizations, Machine Learning and all that, Igor Carron
• #8: Paris Machine Learning Meetup #8: Finding a needle in a Haystack, Beyond SGD, Analyser Wikipedia, Kolibree, Winning Kaggle "Dogs vs Cats" (February 12th, 2014, held at and sponsored by DojoCrea)
• Blog entry presenting the meetup
• The program 
• Presentation of the meetup, Franck Bardol, Frederic Dembak, Igor Carron
• Lenka Zdeborova, IPT, CEA, How hard is it to find a needle in a haystack?
• Francis Bach, INRIA, Beyond stochastic gradient descent for large-scale machine learning.
• Guillaume Pitel, eXenSa, Analyzing Wikipedia with NCISC From (almost) every conceivable angle / Analyser Wikipedia en long, en large, et en travers avec NCISC
• Loïc Cessot, Kolibree, The world's first connected toothbrush.
• Pierre Sermanet , NYU, Winning Kaggle's Dog's vs Cats (remote presentation from New York)
• Organizers: Franck Bardol, Frederic Dembak, Igor Carron
• #7: VMX , Atheer One,VLAD and What does it take to win the Kaggle/Yandex competition ? (January 15th, 2014.held at and sponsored by DojoCrea). 
• Summary of the meetup: "We already have cats under control"
• Video of the Meetup
• The program:
• Tomasz Malisiewicz, VMX Project: Computer Vision for Everyone (remote presentation from Boston)
• Allen Yang, Atheer One, what it feels like to have superpowers (remote presentation from Mountain View) 
• Patrick Perez From image to descriptors and back again
• Kenji Lefèvre-Hasegawa , 'Dataiku Science Studio', What does it take to win the Kaggle/Yandex competition ?
• 
  
• #6: Playing with Kaggle/ Botnet detection with Neural Networks. Jouer avec Kaggle / Detection de Botnets ( December 11th, 2013, held at and sponsored by DojoCrea)
• Presentation, What's New, Franck Bardol, Frederic Dembak, Igor Carron
• Les compétitions Kaggle, un moyen fun et instructif pour mesurer ses compétences en machine learning, Matthieu Scordia
• Réseaux de neurones pour la détection de Botnets, Joseph Ghafari
• Paris Machine Learning Meetup #6 Summary
• Video of the event
• Live Streaming
• Announcement
• Organizers: Franck Bardol, Frederic Dembak, Igor Carron
• #5: Making Sense of Two Data Tsunamis: Genomics and the Internet of Things / La Génomique et l'Internet des Objets  ( November 13, 2013, held at and sponsored by DojoCrea )
• Machine Learning for personalized medicine / Apprentissage statistique pour la médecine personnalisée, Jean-Philippe Vert 
• SARAH by Jean-Philippe Encausse 
• Video of the meetup (in French)
• Organizers: Franck Bardol, Frederic Dembak, Igor Carron
• Summary of the meeting
• Presentation/Context
• Announcement
• Live Streaming, Video provided by Guillaume Pellerin 

• #4: Fake and Real Bayesian Worlds  ( October 16, 2013, held at and sponsored by DojoCrea)
• Andrew Gelman, Modélisation hiérarchique, pooling partiel et l’interrogation de bases de données virtuelles
•  Gabriel Synnaeve, Bayesian Programming and Learning for Multi-Player Video Games
•  Summary Meeting #4,
• Announcement: Paris Machine Learning LinkedIn group and Meetup #4
• Organizers: Franck Bardol, Frederic Dembak, Igor Carron

• #3: Scikit-Learn et Lire dans les pensées (September 17, 2013, held at and sponsored by DojoCrea )
• Machine Learning: What is it good for ? Franck Bardol, Igor Carron
• Scikit-learn: une boite à outils de machine learning, Gaël Varoquaux,
• Mind Reading with Scikit-learn / Lire dans les pensées avec le Scikit-learn, Alexandre Gramfort, 
• Summary: Video: Paris Machine Learning Meetup #3, 
• Announcement: Ce soir: Paris Machine Learning Meetup #3: Lire dans les pensées grâce à Scikit-learn
• Organizers: Franck Bardol, Frederic Dembak, Igor Carron
• Video provided by Guillaume Pellerin 

• #2: Machine Learning Use Case: Apprentissage renforcement appliqué aux telecoms (July 4th, OCTO Technology )
• Association problem in wireless networks : Policy Gradient Reinforcement Learning approach, Stephane Senecal.
• Organizers: Franck Bardol

• #1: Machine Learning, First get together meeting ( June 5th, OCTO Technology )
• Organizers: Franck Bardol

• Announcement: Paris Meetup Groups: Machine Learning and GraphLab

General links:

• Meetup.com page
• LinkedIn Paris Machine Learning Group

Academic Paris based Machine Learning groups

• Statistical Machine Learning in Paris

Internet of Things / Hardware Hacking

• Internet of Things #2 Paris Meetup summary
• Hardware Hacking: ParisEmbedded Meetup #2
• Hardware Hacking: ParisEmbedded Meetup #1

all blog entries related to Meetups.