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Course

PHYS-467: Machine learning for physicists

Lectures in this course (130)

Covers PyTorch tensor data structure and training a CNN to classify images.

Explores the visualization of feature maps in neural networks and the features learned by deep models.

Covers the basics of probability theory, random variables, joint probability, and inference.

Explores linear regression in high dimensions and practical house price prediction from a dataset.

Covers linear regression, regularization, and probabilistic models in generating labels.

Explores linear regression fundamentals, emphasizing the importance of regularization techniques to enhance model performance.

Covers linear regression, regularization, inverse problems, X-ray tomography, image reconstruction, data inference, and detector intensity.

Covers the Curie-Weiss model, discussing its theoretical foundations and practical applications in physical systems.

Covers the basics of machine learning for physicists and chemists, focusing on image classification and dataset labeling.

Covers the fundamentals of statistical mechanics, including system parameters, variables, and thermal equilibrium.

Covers the basics of information theory, entropy, and fixed points in graph colorings and the Ising model.

Focuses on fixed points in graph theory and their implications in algorithms and analysis.

Covers the concepts of Demystification, Estimation, and Bayesian Inference in the context of Bayesian statistics.

Covers unsupervised learning, dimensionality reduction, SVD, low-rank estimation, PCA, and Monte Carlo Markov Chains.

Covers demousing, estimation, Bayesian inference, likelihood, AWGN, and more.

Explores optimization and estimation in Monte Carlo methods, emphasizing Bayes-optimal groups and estimators.

Covers upper and lower bounds, Jensen's inequality, and amended bounds in mathematical analysis.

Covers the theory of Markov chains and Monte Carlo methods.

Covers Monte Carlo Markov Chains and sampling algorithms for iterative trial configurations.

Explores replica symmetry and educated guessing in mathematical models.

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