Courses
Here are some (graduate) physics courses I took that I found interesting
Statistical Physics III
This course introduces statistical field theory, and uses concepts related to phase transitions to discuss a variety of complex systems (random walks and polymers, disordered systems, combinatorial optimisation, information theory and error correcting codes).
Credits: 6/6
Statistical Physics of Computation
Learn statistical physics approach to problems ranging from graph theory (e.g. community detection) to discrete optimization and constraint satisfaction (e.g. satisfiability or coloring) and to inference and machine learning problems (learning in neural networks, clustering of data and of networks, compressed sensing or sparse linear regression, low-rank matrix factorization) is dealt. This is done by theoretical methods of analysis (replica, cavity, ...) algorithms (message passing, spectral methods, etc)
Credits: 6/6
Machine Learning for Physicists
Machine learning and data analysis are becoming increasingly central in sciences including physics. In this course, fundamental principles and methods of machine learning will be introduced and practised.
Credits: 6/6
Statistical Physics IV
Noise and fluctuations play a crucial role in science and technology. This course treats stochastic methods, applying them to both classical problems and quantum systems. It emphasizes the frameworks of fluctuation-dissipation theorems, stochastic differential equations, and Markov processes.
Credits: 5.25/6
Quantum Field Theory I
The goal of the course is to introduce relativistic quantum field theory as the conceptual and mathematical framework describing fundamental interactions.
Credits: 5/6