Lectures in course PHYS-467: Machine learning for physicists

Covers image classification, clustering, and machine learning techniques like dimensionality reduction and reinforcement learning.

Covers the basics of machine learning, including supervised and unsupervised learning, regression, classification, and clustering.

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

Covers the basics of linear regression and how to solve estimation problems using least squares and matrix notation.

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

Covers linear regression for estimating train speed using least squares and regularization.

Provides a Python crash course covering basic arithmetics, complex numbers, strings, lists, dictionaries, and more.

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

Covers linear and ridge regression, overfitting, hyperparameters, and test sets.

Covers the concept of graphical models and joint probability distributions.

Explores probabilistic linear regression, covering joint and conditional probability, ridge regression, and overfitting mitigation.

Covers the basics of Numpy, including importing the library, creating arrays, and performing linear algebra operations.

Covers the probabilistic model for linear regression and its applications in nuclear magnetic resonance and X-ray imaging.

Explains probability distribution, entropy, and Gibbs free entropy, along with the Weiss model.

Covers the basics of polynomial regression and regularization to prevent overfitting.

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

Explores LASSO regression for inducing sparsity in signals through gradient descent.