Covers the fundamental principles and methods of machine learning, including supervised and unsupervised learning techniques.
Introduces data-driven modeling with a focus on regression, covering linear regression, risks of inductive reasoning, PCA, and ridge regression.
Covers the basics of linear regression, binary and multi-class classification, and evaluation metrics.
Introduces nonlinear ML algorithms, covering nearest neighbor, k-NN, polynomial curve fitting, model complexity, overfitting, and regularization.
Introduces the basics of machine learning, covering supervised and unsupervised learning, linear regression, and data understanding.
Explores linear regression, least squares, residuals, and confidence intervals in regression models.
Covers linear regression, regularization, inverse problems, X-ray tomography, image reconstruction, data inference, and detector intensity.
Covers regression analysis for disentangling data using linear regression modeling, transformations, interpretations of coefficients, and generalized linear models.
Covers the basics of linear regression in machine learning, including model training, loss functions, and evaluation metrics.
