Glossary

**K-Means Clustering**: A popular unsupervised learning algorithm that partitions data into K distinct clusters by minimizing the variance within each cluster.

**K-Nearest Neighbors (KNN)**: A simple, instance-based learning algorithm that classifies a data point based on the majority class of its K nearest neighbors in the feature space.

**Kalman Filter**: An algorithm that uses a series of measurements observed over time, containing noise and other inaccuracies, to estimate unknown variables, often used in signal processing and control systems.

**Kernel Function**: A function used in support vector machines and other algorithms to enable operations in a high-dimensional feature space without explicitly computing the coordinates in that space, commonly used in non-linear classification.

**Kernel Density Estimation (KDE)**: A non-parametric way to estimate the probability density function of a random variable, often used to smooth data distributions.

**Kaggle**: An online platform that hosts data science competitions, datasets, and collaborative projects, providing a community and resources for machine learning practitioners.

**Knowledge Base**: A repository of structured information or facts about a particular domain, often used in AI systems to enable reasoning, inference, and decision-making.

**Knowledge Graph**: A network of entities and their interrelations, represented as nodes and edges, used to model real-world knowledge and support tasks like information retrieval and recommendation systems.

**Kurtosis**: A statistical measure that describes the shape of a distribution's tails in relation to its overall shape, often used to detect outliers and understand data distribution properties.

**Kernel Trick**: A method used in machine learning algorithms like support vector machines, where a linear classifier is applied to a non-linear problem by implicitly mapping the data to a higher-dimensional space using a kernel function.

**Kullback-Leibler (KL) Divergence**: A measure of how one probability distribution diverges from a second, expected probability distribution, often used in information theory and machine learning to measure the difference between distributions.

**Kohonen Network**: A type of artificial neural network used for unsupervised learning, also known as a self-organizing map (SOM), that maps high-dimensional data into a lower-dimensional grid while preserving the topological properties of the input space.

**Kernel Principal Component Analysis (KPCA)**: An extension of principal component analysis (PCA) that uses kernel methods to perform non-linear dimensionality reduction.

**K-Fold Cross-Validation**: A model validation technique where the dataset is divided into K subsets, and the model is trained and validated K times, each time using a different subset as the validation set and the remaining as the training set.

**Knowledge Distillation**: A process where a smaller, simpler model (student) is trained to replicate the behavior of a larger, more complex model (teacher), often used to compress models without significant loss of accuracy.

**Kernel Ridge Regression**: A kernelized version of ridge regression that combines ridge regression with the kernel trick, allowing for non-linear relationships between the features and the target variable.

**Krylov Subspace Method**: An iterative method used in numerical linear algebra to solve large systems of linear equations, often used in scientific computing and machine learning for solving optimization problems.

**Knowledge Representation**: The area of artificial intelligence concerned with how to formally think about and represent information in a way that a computer system can use to solve complex tasks.

**Kinematic Chain**: A sequence of rigid bodies connected by joints, used in robotics and animation to model the movement of articulated structures like robot arms or human limbs.

**Kernel Support Vector Machine (KSVM)**: A variant of the support vector machine algorithm that uses a kernel function to handle non-linear relationships in the data, mapping it to a higher-dimensional space where a linear separation is possible.

**Knowledge Discovery in Databases (KDD)**: The overall process of discovering useful knowledge from data, involving data preparation, selection, cleaning, and the application of machine learning algorithms.

**Kendall's Tau**: A statistic used to measure the ordinal association between two measured quantities, often used in statistics to assess the correlation between rankings.

**K-Means++**: An enhancement of the standard K-Means algorithm that improves the initial placement of centroids, leading to better clustering results and faster convergence.

**Knowledge Transfer**: The process of applying knowledge or models learned in one domain or task to another domain or task, often used in transfer learning to leverage pre-trained models.

**Keras**: An open-source software library that provides a Python interface for neural networks, often used to build and train deep learning models, particularly with the TensorFlow backend.

**Kernel PCA**: An extension of Principal Component Analysis (PCA) that applies the kernel trick to perform nonlinear dimensionality reduction, capturing more complex patterns in the data.

**Kinematic Data**: Data related to motion, including position, velocity, and acceleration, often used in biomechanics, robotics, and computer graphics to model and analyze movement.

**Kohonen Map**: Another name for a self-organizing map (SOM), a type of neural network used for dimensionality reduction and clustering that preserves the topological properties of the input space.

**Kaiser-Meyer-Olkin (KMO) Test**: A measure of sampling adequacy that assesses the suitability of data for factor analysis, with higher values indicating that factor analysis may be useful.

**Kolmogorov-Smirnov Test**: A non-parametric test used to compare a sample with a reference probability distribution, or to compare two samples, often used in statistics to test the equality of distributions.

**K-shortest Path**: An algorithmic problem of finding the K shortest paths between a pair of nodes in a graph, often used in network routing and transportation planning.