Co-Learning: Machine Learning - Data Mining - Multivariate Stats

lun. 7 janvier 2019 à 19:15 — informations

lun. 14 janvier 2019 à 19:15 — informations

lun. 21 janvier 2019 à 19:15 — informations

La Permanence
Paris
France
Paris

Chaque semaine le lundi

Hi Data Scientists, Please sign up at 'La Permanence' in order to access to our meetup. We speak French/English... ............................ Who we are? .........................................

The Open Data Science School Paris is a 'co-learning community' on data science. [co-learning? like 'co-working' but to learn...] Examples:

  1. OpenTechSchool [Berlin, Zürich, Brussels, etc. ]

www.opentechschool.org

  1. Advanced-Machine-Learning-Study-Group [Berlin]

www.meetup.com/Advanced-Machine-Learning-Study-Group/

  1. Data Science Speakers Club [London]

www.meetup.com/datasciencespeakers/

  1. LearningGeeks Network [Paris]

www.meetup.com/LearningGeeksNetwork/ It'd be great to have several active co-learning communities on Data Science in Paris!!! Please, you can also start your own co-learning community on writing, strategy, story telling, investing, decision making, programming, math problems, music, etc., whatever you love the most :) .................................... WHAT to do ? ........................................

Come with your own Data Science books/projects/MOOCs that you want to study/work/learn, and we can co-learn together... I'm currently improving my Data Science skills at Univ. Paris Sorbonne and CNAM. So, I can share with you the main ideas/tools I'm learning there... Otherwise, check out these FREE ONLINE RESOURCES: I. In French/English (French Universities):

Prof. Tabea Rebafka notes (Sorbonne University)

https://www.lpsm.paris//pageperso/rebafka/#enseignement

Prof. Maxime Sangnier (Sorbonne University)

http://www.lpsm.paris/pageperso/sangnier/teaching.html

Prof. Ricco Rakotomalala (Université Lumière Lyon 2)

http://ricco-rakotomalala.blogspot.com/

Prof. Philippe Besse (Univ. Toulouse)

https://www.math.univ-toulouse.fr/~besse/enseignement.html

Prof. Francois Husson (Agrocampus Ouest Rennes)

http://math.agrocampus-ouest.fr/infoglueDeliverLive/membres/Francois.Husson/teaching II. In English

( https://github.com/chaconnewu/free-data-science-books#machine-learning ) Understanding Machine Learning: From Theory to Algorithms - Shai Ben-David and Shai Shalev-Shwartz.

( https://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning/understanding-machine-learning-theory-algorithms.pdf ) A First Encounter with Machine Learning ( https://www.ics.uci.edu/~welling/teaching/273ASpring10/IntroMLBook.pdf ) - Max Welling - Beginner Bayesian Reasoning and Machine Learning ( http://web4.cs.ucl.ac.uk/staff/D.Barber/textbook/031013.pdf ) - David Barber - Veteran Sklearn Basics ( http://nbviewer.ipython.org/github/jakevdp/sklearn_scipy2013/tree/master/notebooks/ ) - Beginner Finally, I'd like to share with you this quotation: "Adult learning is more than alternative education, self-help, self-study, or training. Self-directed inquiry can free you from the cultural traps of today’s postmodern world. When you think for yourself, you take control of your life. Intellectual ability and critical thinking soon become substitutes for paper credentials. You'll enjoy a higher quality of life, make smarter career choices, and begin to see ways to better our society. Simply stated aggressive learning is the most practical guide to a passionately rewarding life." Charles D. Hayes

( http://www.autodidactic.com/index.html ) Yours sincerely, Tito Karl M.

Source: https://www.meetup.com/fr-FR/OpenDataScienceSchool-Paris/


La Permanence
Paris
France

Technologie
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