Explores probabilistic linear regression, covering joint and conditional probability, ridge regression, and overfitting mitigation.
Introduces fundamental machine learning concepts, covering regression, classification, dimensionality reduction, and deep generative models.
Covers the basics of machine learning, including supervised and unsupervised techniques, linear regression, and model training.
Covers the basics of linear regression in machine learning, including model training, loss functions, and evaluation metrics.
Explores supervised learning in financial econometrics, covering linear regression, model fitting, potential problems, basis functions, subset selection, cross-validation, regularization, and random forests.
Introduces the basics of machine learning, covering supervised and unsupervised learning, linear regression, and data understanding.
Covers the basics of linear regression, binary and multi-class classification, and evaluation metrics.
