Glossary

**Label Encoding**: A technique used to convert categorical data into numerical values by assigning a unique integer to each category, often used as a preprocessing step for machine learning models.

**Lagrange Multiplier**: A mathematical method used in optimization to find the local maxima and minima of a function subject to equality constraints.

**Laplacian of Gaussian (LoG)**: A feature detection technique used in image processing to detect edges by applying a Gaussian filter followed by a Laplacian operator.

**Latent Dirichlet Allocation (LDA)**: A generative statistical model used in natural language processing to discover the topics in a collection of documents, based on the assumption that documents are mixtures of topics.

**Latent Space**: A lower-dimensional space where high-dimensional data is represented, often used in machine learning models like autoencoders to capture the underlying structure of the data.

**Latent Variable**: A variable that is not directly observed but inferred from other variables, often used in models like factor analysis, hidden Markov models, and variational autoencoders.

**Layer Normalization**: A technique used in neural networks to normalize the inputs across the features within a layer, improving the stability and speed of training.

**Learning Rate**: A hyperparameter that controls the step size at each iteration while moving toward a minimum of the loss function during the training of a machine learning model.

**Learning Rate Decay**: A technique where the learning rate decreases over time during training, often used to allow the model to converge more smoothly.

**Least Absolute Shrinkage and Selection Operator (LASSO)**: A regression technique that performs both variable selection and regularization by adding a penalty equal to the absolute value of the coefficients to the loss function.

**Leave-One-Out Cross-Validation (LOOCV)**: A model validation technique where each data point is used as a validation set once, while the remaining data points are used for training, providing an unbiased estimate of model performance.

**LeNet**: One of the earliest convolutional neural network architectures, designed for handwritten digit recognition, and a foundational model in the field of deep learning.

**Levenshtein Distance**: A metric for measuring the difference between two sequences, defined as the minimum number of single-character edits (insertions, deletions, or substitutions) required to change one sequence into the other.

**LIDAR (Light Detection and Ranging)**: A remote sensing method that uses light in the form of a pulsed laser to measure distances to objects, often used in autonomous vehicles, topography, and geosciences.

**Lift**: A metric used in marketing and recommendation systems to measure the effectiveness of a targeting strategy, defined as the ratio of the results with the strategy to the results without the strategy.

**Likelihood**: A measure of how probable a particular set of parameters is given the observed data, often used in statistical inference to estimate the parameters of a model.

**Linear Discriminant Analysis (LDA)**: A dimensionality reduction technique used in supervised learning to find the linear combination of features that best separates two or more classes.

**Linear Regression**: A basic type of predictive analysis that models the relationship between a dependent variable and one or more independent variables using a linear equation.

**Linear Transformation**: A function between two vector spaces that preserves the operations of vector addition and scalar multiplication, often used in machine learning to project data into different spaces.

**Linear Separability**: A property of a dataset where classes can be separated by a straight line (or hyperplane in higher dimensions), important in models like support vector machines.

**Local Outlier Factor (LOF)**: An algorithm used to identify outliers in a dataset by comparing the local density of a point to that of its neighbors, detecting points that are significantly less dense than their surroundings.

**Local Minimum**: A point in the loss function where the function value is lower than all nearby points, but not necessarily the lowest point overall (global minimum), often encountered in optimization problems.

**Local Response Normalization (LRN)**: A technique used in neural networks to normalize the output of each neuron based on the activity of neighboring neurons, often used in convolutional networks.

**Logistic Regression**: A statistical model used for binary classification tasks, where the output is a probability that a given input belongs to a particular class, modeled using a logistic function.

**Log-Loss (Logarithmic Loss)**: A loss function used in classification tasks that penalizes predictions based on the probability assigned to the correct class, encouraging well-calibrated probabilities.

**Long Short-Term Memory (LSTM)**: A type of recurrent neural network architecture designed to remember information for long periods, overcoming the vanishing gradient problem associated with traditional RNNs, often used in sequence prediction tasks.

**Loss Function**: A mathematical function that quantifies the difference between the predicted output and the actual output of a model, guiding the optimization process during training.

**Low-Rank Approximation**: A method of approximating a matrix by another matrix with lower rank, often used in dimensionality reduction and data compression techniques like Singular Value Decomposition (SVD).

**Label Propagation**: A semi-supervised learning algorithm that spreads labels from labeled to unlabeled data points through a graph representing the dataset, leveraging the structure of the data to improve classification accuracy.

**Latent Semantic Analysis (LSA)**: A technique in natural language processing that analyzes relationships between a set of documents and the terms they contain by producing a set of concepts related to the documents and terms, often used for information retrieval.

**Label Smoothing**: A regularization technique used in classification tasks where the ground truth labels are adjusted to be less confident, reducing overfitting and improving generalization.

**Likelihood-Free Inference**: A method of statistical inference that does not rely on a likelihood function to estimate parameters, often used in situations where the likelihood is intractable or difficult to compute.

**Low-Rank Matrix Factorization**: A technique used in recommendation systems to decompose a user-item interaction matrix into lower-dimensional matrices, capturing latent factors that explain user preferences and item characteristics.

**Latent Factor Model**: A type of model used in recommendation systems where both users and items are represented by latent factors in a shared space, allowing the model to predict user preferences based on their interactions with items.

**Layer-wise Relevance Propagation (LRP)**: A technique used in explainable AI to interpret the decisions made by neural networks by propagating relevance scores back through the layers of the network, highlighting the input features that contributed most to the decision.

**Laplacian Eigenmaps**: A dimensionality reduction technique that uses the eigenvalues of the Laplacian matrix of a graph to embed high-dimensional data into a lower-dimensional space, preserving the local neighborhood structure of the data.

**Linear Classifier**: A classifier that makes predictions based on a linear combination of input features, often used in simple models like logistic regression and support vector machines.

**Learning Curve**: A graph that shows the performance of a machine learning model on the training and validation datasets over time or with varying amounts of training data, used to diagnose underfitting, overfitting, and the need for more data.