💎 “Machine-learning techniques can extract abstract physical concepts and consequently become an integral part of theory- and model-building.”

https://www.nature.com/articles/s41567-018-0081-4

🌲 Tree crown shyness – a photogenic phenomenon in which the crowns of fully grown trees do not touch each other. A metaphor for so many things, it really puts you to think what it means to interact.

🌲 Trees as islands: canopy ant species richness increases with the size of liana‐free trees in a Neotropical forest
https://onlinelibrary.wiley.com/doi/abs/10.1111/ecog.02608

🎞 https://youtu.be/c0gDLEHbYCk http://www.chaos-math.org/en

💡 https://medium.com/@FelixSimon/cambridge-analytica-and-the-big-data-panic-5029f12e1bcb

Last day to apply:
https://www.icts.res.in/program/integrability2018

Responding To Complexity: A Discussion With Yaneer Bar-Yam

✅ https://physics.aps.org/synopsis-for/10.1103/PhysRevLett.120.138301

🔖 A high-bias, low-variance introduction to Machine Learning for physicists

Pankaj Mehta, Marin Bukov, Ching-Hao Wang, Alexandre G.R. Day, Clint Richardson, Charles K. Fisher, David J. Schwab

https://arxiv.org/pdf/1803.08823

Machine Learning (ML) is one of the most exciting and dynamic areas of modern research and application. The purpose of this review is to provide an introduction to the core concepts and tools of machine learning in a manner easily understood and intuitive to physicists. The review begins by covering fundamental concepts in ML and modern statistics such as the bias-variance tradeoff, overfitting, regularization, and generalization before moving on to more advanced topics in both supervised and unsupervised learning. Topics covered in the review include ensemble models, deep learning and neural networks, clustering and data visualization, energy-based models (including MaxEnt models and Restricted Boltzmann Machines), and variational methods. Throughout, we emphasize the many natural connections between ML and statistical physics. A notable aspect of the review is the use of Python notebooks to introduce modern ML/statistical packages to readers using physics-inspired datasets (the Ising Model and Monte-Carlo simulations of supersymmetric decays of proton-proton collisions). We conclude with an extended outlook discussing possible uses of machine learning for furthering our understanding of the physical world as well as open problems in ML where physicists maybe able to contribute. (Notebooks are available at this https URL: https://physics.bu.edu/~pankajm/MLnotebooks.html)

❄ https://www.nature.com/articles/s41567-018-0077-0

🤔 https://www.nature.com/articles/d41586-018-03925-8

💻 A Free Oxford Course on Deep Learning: Cutting Edge Lessons in Artificial Intelligence

Slides and Videos:
🔗 https://www.cs.ox.ac.uk/people/nando.defreitas/machinelearning/

این گروه جهت اطلاع رسانی و معرفی برنامه‌های سمپوزیوم علوم اعصاب نظری و محاسباتی ایجاد شده است. این همایش با همکاری دانشگاه فرایبورگ آلمان، در تاریخ بیست و هفتم و بیست و هشتم فروردین ماه سال 1397 در محل دانشکده مهندسی برق و کامپیوتر دانشگاه تهران، برگزار خواهد شد.
https://goo.gl/Tgm6wX

📌 علاقمندان جهت ثبت نام در این همایش، رزومه خود را به نشانی زیر ارسال کنند:

ut.tcns2018@gmail.com

💭 So many great mathematicians, from Archimedes, Bernoulli, Lagrange, to Pontryagin and Lions have put their stamp on "optimization" and "optimal control" problems. Here is a great summary.

www.unige.ch/~gander/Preprints/LagrangeTalk.pdf

✅ When mathematicians write a review on statistical physics theories it gets pretty exciting:

🔗 arxiv.org/pdf/1803.11132.pdf

🎞 Machine Learning Summer School 2017 Tübingen
Max Planck Institute for Intelligent Systems

33 Videos:
https://www.youtube.com/watch?v=XLHB-Aktxw0&list=PLqJm7Rc5-EXFUOvoYCdKikfck8YeUCnl9

🔸 An Introduction to Graphical Lasso
Bo Chang
Graphical Models Reading Group
May 15, 2015

Slides:
https://www.stat.ubc.ca/~bchang/gmrg/files/Bo_05152015.pdf