SLPs and Machine

Machine Learning (ML)

A branch of computer science focused on the development of models and algorithms that learn from data to improve predictive performance.

Artificial Intelligence (AI)

A broader field than ML that focuses on equipping machines with capacities to approximate humanlike intelligence such as communication, reasoning, and perception.

Algorithm

A sequence of rules executed by a computer to solve a problem.

Dataset

A collection of data from which ML models learn.

Model

A program that can find patterns or make decisions from previously unseen data; it represents what algorithms learned from training data.

Feature

An independent property used as input to the model.

Label/Target

The predetermined outcome to be predicted by the model.

Training

The process whereby the model learns the hidden pattern and relationship existing in the features and how they relate to the target value.

Testing

The process of evaluating the final performance of a model with optimal configuration on previously unseen data.

Classification

The goal of a classification task is to predict a discrete label from two or more categories based on input features.

Regression

The goal of a regression task is to predict a continuous numeric value based on input features.

Supervised Learning

Algorithms characterized by labeled input data, meaning the training data includes both features and the correct output.

Unsupervised Learning

Algorithms trained to detect patterns and similarities in unlabeled data sets, containing only features without predefined outcomes.

Neural Network

A powerful ML method inspired by the human brain, consisting of interconnected groups of artificial neurons that can learn complex functions.

Evaluation Metric

A measure used to quantify the performance of an ML model based on its predictions compared to actual outcomes.