PHYS-467: Machine learning for physicists

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Lectures in this course (130)

Introduction to Image Classification Log in to Mediaspace to watch this video

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

Linear Regression: Basics and Estimation Log in to Mediaspace to watch this video

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

Regression Again: Exercise 3.1 Log in to Mediaspace to watch this video

Covers exercises on regression, including linear and polynomial regression, high dimensions, and real data analysis.

Inference: Exercise 3.2 Log in to Mediaspace to watch this video

Covers the topic of likelihood estimation and maximum likelihood estimation in inference.

Sparse Regression Log in to Mediaspace to watch this video

Covers the concept of sparse regression and the use of Gaussian additive noise in the context of MAP estimator and regularization.

Gradient Descent Log in to Mediaspace to watch this video

Covers the concept of gradient descent, a universal algorithm used to find the minimum of a function.

Regression Reloaded: Synthetic Dataset Construction and Parameter Estimation Log in to Mediaspace to watch this video

Covers the construction of a synthetic dataset for linear regression and the estimation of ground truth parameters.

L2 Regularization in Diabetes Dataset Analysis Log in to Mediaspace to watch this video

Covers the application of L2 regularization in analyzing the diabetes dataset.

Logistic Regression: Classification Log in to Mediaspace to watch this video

Covers supervised learning, classification using logistic regression, and challenges in optimization.

Linear Classification: Logistic Regression Log in to Mediaspace to watch this video

Covers linear classification using logistic regression, regularization, and multiclass classification.

Lasso and MNIST Basics Log in to Mediaspace to watch this video

Introduces Lasso regularization and its application to the MNIST dataset, emphasizing feature selection and practical exercises on gradient descent implementation.

Unsupervised Learning: Principal Component Analysis Log in to Mediaspace to watch this video

Covers unsupervised learning with a focus on Principal Component Analysis and the Singular Value Decomposition.

Python Crash Course Log in to Mediaspace to watch this video

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

Unsupervised Learning: Movie Recommendation Log in to Mediaspace to watch this video

Covers unsupervised learning for movie recommendation using singular value decomposition.

Gradient Descent: MNIST Dataset and Logistic Loss Log in to Mediaspace to watch this video

Focuses on implementing gradient descent with the MNIST dataset and logistic loss in machine learning.

Gradient Descent: Early Stopping and Stochastic Gradient Descent Log in to Mediaspace to watch this video

Explains gradient descent with early stopping and stochastic gradient descent to optimize model training and prevent overfitting.

Spin Glasses and Bayesian Estimation Log in to Mediaspace to watch this video

Covers the concepts of spin glasses and Bayesian estimation, focusing on observing and inferring information from a system closely.

Markov Chain Monte Carlo Log in to Mediaspace to watch this video

Explains the Markov Chain Monte Carlo method and the Metropolis-Hastings algorithm for sampling.

Principal Component Analysis: Eigenfaces Log in to Mediaspace to watch this video

Covers the application of Principal Component Analysis in facial recognition using a famous faces dataset.

Biased Monte Carlo Markov Chain Log in to Mediaspace to watch this video

Explores Biased Monte Carlo Markov Chain, including Bayes-optimal estimation and Metropolis-Hastings algorithm.

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