Glossary

**Q-Learning**: A model-free reinforcement learning algorithm that learns the value of an action in a particular state, aiming to maximize the total reward over time by updating Q-values based on the Bellman equation.

**Quantization**: The process of mapping a large set of input values to a smaller set, often used in machine learning to reduce the size of models by converting floating-point weights to lower precision formats like 8-bit integers.

**Quadratic Discriminant Analysis (QDA)**: A classification technique that generalizes linear discriminant analysis by allowing the decision boundary to be a quadratic function, modeling each class with its own covariance matrix.

**Quantile**: A statistical measure that divides a dataset into intervals with equal probabilities, with specific quantiles like quartiles, percentiles, and deciles used to summarize the distribution of data.

**Quantile Regression**: A type of regression analysis used to estimate the conditional quantiles of a response variable, providing a more comprehensive view of possible outcomes beyond the mean prediction.

**Query Expansion**: A technique used in information retrieval to improve search results by expanding the user's query with additional relevant terms or synonyms, increasing the likelihood of retrieving relevant documents.

**Query Understanding**: The process of interpreting and refining user queries in search engines or databases to improve the relevance of the returned results, often involving natural language processing techniques.

**Queueing Theory**: A branch of mathematics that studies the behavior of queues or waiting lines, often used in operations research, telecommunications, and computer networks to optimize resource allocation and reduce wait times.

**Quasi-Newton Method**: An optimization algorithm that approximates the Hessian matrix to achieve faster convergence than gradient descent, often used in large-scale optimization problems where computing the exact Hessian is computationally expensive.

**Quasi-Random Sequence**: A sequence of numbers that approximates the properties of random numbers but with more uniform distribution, often used in numerical integration and sampling.

**Query by Committee (QBC)**: An active learning strategy where multiple models (the committee) are used to evaluate the uncertainty of predictions, and the most uncertain instances are selected for labeling, improving the efficiency of the learning process.

**Quantum Computing**: A field of computing that leverages the principles of quantum mechanics to process information in ways that classical computers cannot, offering the potential for solving certain complex problems more efficiently.

**Quincunx (Galton Board)**: A device that demonstrates the central limit theorem and normal distribution by dropping balls through a series of pins, showing how random processes can lead to a normal distribution.

**Quotient Space**: In mathematics, a space obtained by partitioning a larger space into equivalence classes, often used in topology, group theory, and algebraic geometry.

**Qubit**: The basic unit of quantum information, representing a quantum state that can exist as a 0, 1, or any superposition of these states, forming the foundation of quantum computing.

**Quasi-Monte Carlo Method**: A numerical method that uses low-discrepancy sequences instead of random sampling to improve the accuracy and efficiency of Monte Carlo simulations, often used in financial modeling and computational physics.

**Q-Learning with Experience Replay**: An enhancement to the standard Q-learning algorithm where past experiences are stored and replayed to improve learning stability and efficiency, commonly used in deep reinforcement learning.

**Quadratic Programming (QP)**: An optimization problem where the objective function is quadratic, and the constraints are linear, often used in portfolio optimization, support vector machines, and resource allocation.

**Quantitative Structure-Activity Relationship (QSAR)**: A method used in cheminformatics and drug design to predict the activity or properties of molecules based on their chemical structure using statistical and machine learning techniques.

**Quiescence Search**: A technique used in game tree search algorithms to extend the search in "noisy" positions until a quiet position is reached, reducing the risk of making poor decisions based on incomplete evaluations.

**Quantum Machine Learning**: A subfield of machine learning that explores the use of quantum algorithms and quantum computing to enhance machine learning models, offering potential speedups for certain tasks.

**Query Optimization**: The process of improving the efficiency of query processing in databases by selecting the most efficient execution plan, often involving techniques like indexing, join optimization, and query rewriting.