Design Optimisation and Orthogonality: Multilinear Regression
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Explores optimization methods like gradient descent and subgradients for training machine learning models, including advanced techniques like Adam optimization.
Covers the basics of Machine Learning, including recognizing hand-written digits, supervised classification, decision boundaries, and polynomial curve fitting.
Explores logistic regression fundamentals, including cost functions, regularization, and classification boundaries, with practical examples using scikit-learn.
Covers regression diagnostics for linear models, emphasizing the importance of checking assumptions and identifying outliers and influential observations.
Delves into the trade-off between model flexibility and bias-variance in error decomposition, polynomial regression, KNN, and the curse of dimensionality.
