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Course

PHYS-467: Machine learning for physicists

Lectures in this course (130)

Covers the simplest model for overlapping events and their probabilities.

Introduces the Boltzmann Machine, covering expectation consistency, data clustering, and probability distribution functions.

Covers the concept of symmetry in physics and its applications in various contexts.

Covers the Stochastic Block Model for community detection.

Covers kernel regression and feature maps for data separability.

Covers the application of Belief Propagation in Stochastic Block Models, focusing on simplifying the process and solving it step by step.

Covers the concept of linear regression, including polynomial regression and hyperparameters selection.

Covers graphical models for probabilistic distributions using graphs, nodes, and edges.

Covers the computation of posterior distributions in Bayesian inference.

Covers random features in neural networks and kernel regression equivalence.

Explores optimal detection in spinodal systems, discussing stability analysis and phase transitions.

Explores neural networks, hidden layers, weight adjustments, activation functions, and the universal approximation theorem.

Explores the transition from sparse to dense graphs and their real-world applications.

Explores Convolutional Neural Networks, focusing on layers, filters, pooling, and weight sharing.

Covers the Spin Glass Game, belief propagation, and message passing algorithms.

Explains Transformer networks and self-attention layers for mapping inputs and multi-head attention.

Covers the backpropagation algorithm for training neural networks and the representation of functions in multilayer networks.

Compares random and symmetrical graph coloring in terms of cluster colorability and equilibrium.

Explores the benefits of deeper networks in deep learning and the importance of over-parameterization and generalization.

Explores the statistical physics of clusters, focusing on complexity and equilibrium behavior.

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