Design of Experiments: Response Surface

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Introduces linear models in machine learning, covering basics, parametric models, multi-output regression, and evaluation metrics.

Hypothesis Testing and Non-Parametric Regression

Covers worked examples on hypothesis testing and non-parametric regression.

Regularization in Machine Learning

Introduces regularization techniques to prevent overfitting in machine learning models.

Optimization in Statistics and Machine Learning: Maximum Likelihood Estimation

Explores Maximum Likelihood Estimation, linear models, logistic regression, and Support Vector Machines.

Linear Models and Overfitting

Explores linear models, overfitting, and the importance of feature expansion and adding more data to reduce overfitting.

Design Optimisation and Orthogonality: Multilinear Regression

Explores multilinear regression for design optimization and orthogonality, covering teamwork, abstracts, linear and quadratic models, ANOVA, and alias structures.

ANOVA and Factorial Experiments

Explores ANOVA, factorial experiments, and model evaluation techniques.

Linear Models for Classification

Explores linear models for classification, logistic regression, and gradient descent in machine learning.

Non-Negative Definite Matrices and Covariance Matrices

Covers non-negative definite matrices, covariance matrices, and Principal Component Analysis for optimal dimension reduction.

Linear Regression: Mean-square-error Inference

Covers the MSE problem in linear regression models, focusing on the optimal estimator and data fusion methods.

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