IB Computer Science Topic A4: Machine Learning Key Definitions

The function a node applies to its weighted sum to produce its output. It introduces non-linearity, which is what allows a network of layers to model more than a single straight boundary.

The property of a machine learning system whose outcomes do not depend on group membership such as sex, ethnicity or age.

An application-specific integrated circuit (ASIC) is a chip designed and fabricated to carry out one fixed task. It cannot be reprogrammed after manufacture, which is what makes it fast and power-efficient on that task and useless if the task changes.

A method for association rule learning that first finds every itemset meeting a minimum support, then builds rules from those itemsets and keeps the ones meeting a minimum confidence. It prunes the search using the property that any set containing an infrequent itemset must itself be infrequent.

A computational model built from interconnected nodes, or neurons, loosely inspired by the brain.

An if-then statement reporting that two sets of items tend to appear in the same transaction, written $\{\text{antecedent}\} \Rightarrow \{\text{consequent}\}$. It records co-occurrence in the data and makes no claim about cause.

A technology that overlays digital content such as images, sounds, or data onto the real-world environment, enhancing how users perceive and interact with their surroundings.

The training step that works backwards from the loss at the output layer, calculating how much each weight and bias contributed to the error so gradient descent can adjust them.

An unfair tendency for or against particular individuals or groups. In machine learning it is learned from patterns already present in the training data.

A learned constant added to a node's weighted sum, which shifts the threshold at which the node fires so its decision boundary need not pass through the origin.

The mean position of all the data points assigned to a cluster. It is a calculated position rather than an observation, so usually no data point sits exactly on it.

Clustering is a technique used to group a set of objects so that objects in the same group (or cluster) are more similar to each other than to those in other groups.

A measure of model fit, written $r^2$, giving the share of the variation in the dependent variable that the model accounts for. It runs from 0, no better than always predicting the mean, to 1, a line passing through every observed point.

A recommendation method that treats each user as a point defined by their ratings, finds the users nearest to them, and suggests items those neighbours rated highly.

For an association rule, the share of the transactions containing the antecedent that also contain the consequent. It equals the support of the whole itemset divided by the support of the antecedent alone.

A table of the four outcome counts of a two-class prediction: true positives, false positives, false negatives and true negatives.

A neural network designed for data with spatial structure, above all images.

A way of estimating performance that splits the data several times, uses a different slice as the validation set each round, and averages the results.

Crossover, also called recombination, is the operator that combines parts of two parent solutions to produce offspring, so features that already score well can be mixed into one candidate.

The sum of every reward a reinforcement learning agent collects across a complete episode, which is the quantity the agent tries to maximise.

The set of problems that appear as the number of features grows, because the space the data describes expands exponentially while the number of records does not.

The stage of data preprocessing where errors, gaps, duplicates and inconsistencies are removed from a data set before a model is trained.

A density-based clustering algorithm that groups points packed closely together, using a neighbourhood radius epsilon and a minimum neighbour count minPts, and labels points in sparse regions as outliers. It finds clusters of any shape and does not need the number of clusters in advance.

A supervised learning model that classifies an example by testing one feature at a time and following the branch that matches, until it reaches a leaf carrying a label.