Explains the construction of natural cubic splines, emphasizing smoothness and continuity in the function representation.
Covers the linear mixed model, including fixed and random effects, estimation, and inference techniques.
Explores probabilistic linear regression, covering joint and conditional probability, ridge regression, and overfitting mitigation.
Covers linear models, including regression, derivatives, gradients, hyperplanes, and classification transition, with a focus on minimizing risk and evaluation metrics.
Covers Multilayer Perceptrons, artificial neurons, activation functions, matrix notation, flexibility, regularization, regression, and classification tasks.
