Compulsory courses, M2 First semester, courses
Computational science
This lecture introduces the students to the use of computing tools, with applications ranging from statistical physics to complex systems and modern subjects such as machine learning. The course is accompanied by tutorials, and students are given homework to program by themselves. We also attempt to introduce the students to modern applied mathematics and statistics (frequentist and Bayesian) and neural networks, and seek a fined-tune balance between mathematics, physics, algorithms and programming.
Bibliography
- Statistical Mechanics: Algorithms and Computations, W. Krauth, Oxford University Press.
- The Elements of Statistical Learning, T. Hastie, R. Tibshirani & J. Friedman, Springer.
- All of statistics a concise course in statistical inference, L. Wasserman, Springer.
Florent Krzakala
(Université Pierre et Marie Curie)
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