Statistical Physics Studies of Machine Learning Problems:
> What makes problems studied in machine and statistical physics related? How can this relation be used to understand better the performance and limitations of machine learning systems? What happens when a phase transition is found in a computational problem? How do phase transitions influence algorithmic hardness?
[Using Physical Insights for ML](http://www.ipam.ucla.edu/programs/workshops/workshop-iv-using-physical-insights-for-machine-learning/?tab=overview)
Cracking big data with statistical physics(About) > Models studied in statistical physics are mathematically equivalent to some of those in high-dimensional statistics.
> Statistical physics is often concerned with phase transitions, i.e., abrupt changes in behaviour. Interestingly, there is a deep correspondence between physical phases such as liquid, super-cooled liquid or glass, and solid, and regions of parameters for which a given data analysis task is algorithmically impossible, hard or easy