Classical ML
Foundational supervised learning, optimisation, interpretability, and statistical machine learning topics. 6 topics in this category.
beginner
Classical Interpretability Techniques
A survey of classical and model-agnostic techniques for understanding how machine learning models make predictions.
beginnerGradient Descent
Iterative first-order optimisation algorithm for differentiable loss functions.
beginnerLinear Regression
Fitting a linear model to data using least squares optimisation.
beginnerProbability And Bayesian Inference
the bayesian perspective of things
intermediateSupport Vector Machines
Maximum-margin classifiers using kernel methods for non-linear decision boundaries.
beginnerBayesian View on Regularization
Understanding ridge and lasso regularization through Bayesian priors and maximum a posteriori estimation.