Co-Learning: Machine Learning - Data Science - Multivariate Stats.
Détails
Hi Data Scientists,
............................ Who we are? .........................................
We're a 'co-learning community' on data science. [co-learning? like 'co-working', but to learn...]
Examples:
- OpenTechSchool [Berlin, Zürich, Brussels, etc. ]
www.opentechschool.org - Advanced-Machine-Learning-Study-Group [Berlin]
www.meetup.com/Advanced-Machine-Learning-Study-Group/ - Data Science Speakers Club [London]
www.meetup.com/datasciencespeakers/
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 any subject such as: writing, strategy, investing, programming, maths problems, music, etc., whatever you love the most :)
.................................... WHAT to do ? ........................................
Come with your own Data Science books/projects/MOOC's that you want to study/work/learn, and we can co-learn together. We are open to questions, comments, share ideas, etc.
We're currently reviewing the following books/notes/resources
- Maths of Machine Learning (MIT Fall 2015)
https://ocw.mit.edu/courses/mathematics/18-657-mathematics-of-machine-learning-fall-2015/index.htm - L'apprentissage par réseaux de neurones profonds. (College de France Winter 2019)
https://www.college-de-france.fr/site/stephane-mallat/course-2018-2019.htm - Data Science en pratique (Sorbonne University UPMC Winter 2019)
https://sites.google.com/site/arthurllau/enseignements - Learning from data (Caltech Spring 2012)
https://work.caltech.edu/telecourse.html - Topics in Mathematics with Applications in Finance (MIT Fall 2013)
(It contains Time Series Analysis, etc.)
https://ocw.mit.edu/courses/mathematics/18-s096-topics-in-mathematics-with-applications-in-finance-fall-2013/index.htm
BONUS
6. Quantum Theory (Friedrich-Alexander-Universität Fall 2015) (I know it's not Data Sci, but still is a very good course!. And, QT uses a lot of probability theory...)
https://mathswithphysics.blogspot.com/2016/07/frederic-schullers-lectures-on-quantum.html
Otherwise, you have FREE ONLINE RESOURCES:
I. French/English
Prof. Tabea Rebafka notes (Sorbonne University)
https://www.lpsm.paris//pageperso/rebafka/#enseignement
Prof. Ricco Rakotomalala (Université Lumière Lyon 2)
http://ricco-rakotomalala.blogspot.com/
Prof. Francois Husson (Agrocampus Ouest Rennes)
http://math.agrocampus-ouest.fr/infoglueDeliverLive/membres/Francois.Husson/teaching
II. English
(https://github.com/chaconnewu/free-data-science-books )
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
"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.