Backpropagation

Ridge Regression

A regularised regression technique that adds an L2 penalty term to prevent overfitting by constraining coefficient magnitudes.

Elastic Net

A regularisation technique combining L1 and L2 penalties, balancing feature selection and coefficient shrinkage.

Cross-Validation

A resampling technique that partitions data into subsets, training on some and validating on others to assess model generalisation.

Overfitting

When a model learns the training data too well, including noise, resulting in poor performance on unseen data.

Underfitting

When a model is too simple to capture the underlying patterns in the data, resulting in poor performance on both training and test data.

Bias-Variance Tradeoff

The balance between a model's ability to minimise bias (error from assumptions) and variance (sensitivity to training data fluctuations).

Regularisation

Techniques that add constraints or penalties to a model to prevent overfitting and improve generalisation to new data.

Gradient Descent

An optimisation algorithm that iteratively adjusts parameters in the direction of steepest descent of the loss function.

Stochastic Gradient Descent

A variant of gradient descent that updates parameters using a randomly selected subset of training data each iteration.

Adam Optimiser

An adaptive learning rate optimisation algorithm combining momentum and RMSProp for efficient deep learning training.

Learning Rate

A hyperparameter that controls how much model parameters are adjusted with respect to the loss gradient during training.

Loss Function

A mathematical function that measures the difference between predicted outputs and actual target values during model training.

A simple algorithm that classifies data points based on the majority class of their k closest neighbours in feature space.

A subset of the training data used to compute a gradient update during stochastic gradient descent.

A tree-structured model where internal nodes represent feature tests, branches represent outcomes, and leaves represent predictions.

Techniques that reduce the number of input variables in a dataset while preserving essential information and structure.

A probabilistic classifier based on applying Bayes' theorem with the assumption of independence between features.

A technique where knowledge gained from training on one task is applied to a different but related task.

Uniform Manifold Approximation and Projection — a dimensionality reduction technique for visualisation and general non-linear reduction.

A partitioning algorithm that divides data into k clusters by minimising the distance between points and their cluster centroids.

Neural Network

A computing system inspired by biological neural networks, consisting of interconnected nodes that process information in layers.