The course will deal with subjects of modern statistical mechanics useful in data science, like mean-field methods, disordered systems and spin glasses, problems of combinatorial optimisation, statistical mechanics of networks, inference.
Expected learning outcomes
The student will learn to use in an independent way the tools of interdisciplinary statistical mechanics, with application to biology, computer science, ecology, etc.
Lesson period: Second semester
(In case of multiple editions, please check the period, as it may vary)
The course content can be divided into (i) a core of central arguments, which includes the relationships between statistical mechanics and probability theory, the understanding and use of mean-field methods, and the out-of-equilibrium dynamics and complex systems, (ii) a set of modules that present varied models and fields of application (and can change year by year, even at the request of students)
Prerequisites for admission
A previous knowledge of elementary Statistical Physics is useful.
Frontal interactive lectures / exercises (it is recommended to attend)
See the slack space: statmech2.slack.com
The course is not based on any specific book, but the books by Peliti and Sethna can be a useful starting point for an independent study
L. Peliti Statistical Mechanics in a Nutshell (Princeton University Press, 2011) J. Sethna Statistical Mechanics: Entropy, Order Parameters and Complexity (Oxford)
Assessment methods and Criteria
The exam consists of an oral discussion that focuses on the topics covered in the course, addressing the ability of the student to elaborate a synthetic view of the subject and to explore independently a topic of choice.