A type of machine learning where models learn patterns from unlabelled data without explicit outputs.

A technique used to group similar data points based on features, often used in unsupervised learning.

A popular clustering algorithm that partitions data into K distinct clusters based on feature similarity.

The process of assigning a label to an image based on its content, often using machine learning techniques.

The process of transforming raw data into a set of measurable features, crucial for improving model accuracy.

Scale-Invariant Feature Transform, a method for detecting and describing local features in images.

Histogram of Oriented Gradients, a feature descriptor used for object detection in images.

Density-Based Spatial Clustering of Applications with Noise, a clustering method that identifies dense regions in data.

A type of machine learning where models are trained on labelled data, learning to predict outputs.

A method to evaluate the performance of different models or techniques against each other.

The practice of ensuring fairness, accountability, and transparency in AI applications.

The graphical representation of data to identify patterns, trends, and insights effectively.

The process of assessing the performance of a machine learning model using various metrics.

A set of rules or processes followed in calculations or problem-solving operations, especially by a computer.

Comparing a system's performance against established standards or best practices.

The process of optimizing the parameters of a model to improve its performance.

Techniques that automate the process of applying machine learning to real-world problems.

A technique for assessing how the results of a statistical analysis will generalize to an independent dataset.

A modeling error that occurs when a model learns the training data too well, failing to generalize.

A modeling error that occurs when a model is too simple to capture the underlying trend of the data.

Techniques used to increase the diversity of training data without actually collecting new data.

A machine learning technique where a model developed for one task is reused for a different but related task.

A formal document presenting original findings and contributions to the field, often for publication.

A software framework that provides tools and libraries for building machine learning models.

A high-level programming language widely used for machine learning and data analysis.

A programming language and software environment used for statistical computing and graphics.