Explores the Gaussian conditional model for linear regression and the properties of Gaussian data, illustrated with the example of kidney stone treatment comparison.
Explores linear regression from a statistical inference perspective, covering probabilistic models, ground truth, labels, and maximum likelihood estimators.
Introduces the fundamentals of regression in machine learning, covering course logistics, key concepts, and the importance of loss functions in model evaluation.
Introduces state-of-the-art methods in optimization and simulation, covering topics like statistical analysis, variance reduction, and simulation projects.
Covers optimization in machine learning, focusing on gradient descent for linear and logistic regression, stochastic gradient descent, and practical considerations.
