Computational Physics (mod. B)

This is the repository for the exercises of the course Laboratory of Computational Physics, mod. B.

Topics covered

• Gradient descent
• Deep neural networks
• Convolutional neural networks
• Combining models, focus on XGBoost
• Data visualization and clustering
• Unsupervised learning and Boltzmann machines

References

• Pankaj Mehta et al. “A high-bias, low-variance introduction to Machine Learning for physicists”. In: Physics Reports 810 (2019), 1–124. issn: 0370-1573. doi: 10.1016/j.physrep.2019. 03.001. url: http://dx.doi.org/10.1016/j.physrep.2019.03.001.
• Notebooks of the above review: https://physics.bu.edu/~pankajm/MLnotebooks.html