Statistical Physics of LearningOffers insights into the statistical physics of learning, exploring the relationship between neural network structure and disordered systems.
Machine Learning in Molecular DynamicsExplores machine learning in molecular dynamics simulations, addressing the curse of dimensionality, neural network representation, and force-field estimation.
Curie Weiss ModelCovers the Curie-Weiss model in Statistical Physics, including magnetization probability, free entropy, and the cavity method.
Applications of GAMPDelves into applying the GAMP algorithm to simplify the lasso problem and analyze optimization challenges in neural networks.
Spike Wigner ModelExplores the Spike Wigner model, Bayesian denoising, state evolution, and spectral methods in matrix analysis.
Replica and Symmetry BreakingExplores the Nobel Prize-winning discovery of replica and cavity methods in complex systems, focusing on the random energy model and the application of probability theory.
Nonlinear Supervised LearningExplores the inductive bias of different nonlinear supervised learning methods and the challenges of hyper-parameter tuning.
Financial Time Series AnalysisCovers stylized facts of asset returns, summary statistics, testing for normality, Q-Q plots, and efficient market hypothesis.
Deep Learning FundamentalsIntroduces deep learning fundamentals, covering data representations, neural networks, and convolutional neural networks.