Q-Learning
A reinforcement learning algorithm that learns optimal actions by estimating Q-values.
Types of Q-Learning
- Tabular Q-Learning - Uses lookup tables for small state spaces.
- Deep Q-Networks (DQN) - Uses deep learning for large state spaces.
Example
Used in game-playing AI like AlphaGo.
Quadratic Programming
A mathematical optimization technique used in support vector machines (SVMs).
Types of Quadratic Programming
- Convex Quadratic Programming - Solvable using standard algorithms.
- Non-Convex Quadratic Programming - More complex to solve.
Example
Used in optimizing SVM classifiers.
Quantile Regression
A regression technique that estimates conditional quantiles instead of the mean.
Types of Quantile Regression
- Linear Quantile Regression - Uses linear models.
- Nonlinear Quantile Regression - Uses nonlinear models.
Example
Used in financial risk modeling.
Query Expansion
A technique in information retrieval that improves search results by modifying queries.
Types of Query Expansion
- Relevance Feedback - Uses user feedback for improvement.
- Automatic Query Expansion - Uses synonyms and related terms.
Example
Used in search engines like Google.
Quasi-Newton Methods
Optimization algorithms that approximate Newton’s method for faster convergence.
Types of Quasi-Newton Methods
- BFGS - A popular optimization algorithm.
- Limited-Memory BFGS (L-BFGS) - A memory-efficient variant.
Example
Used in deep learning model optimization.
Quantum Machine Learning
The study of quantum computing techniques to improve ML algorithms.
Types of Quantum ML
- Quantum Support Vector Machines - Uses quantum kernels.
- Quantum Neural Networks - Leverages quantum circuits.
Example
Used in developing quantum-enhanced AI models.
Quasi-Random Sampling
A sampling method that generates points more uniformly than pure random sampling.
Types of Quasi-Random Sampling
- Halton Sequence - Common in Monte Carlo methods.
- Hammersley Sequence - Ensures better coverage of space.
Example
Used in Monte Carlo simulations.
Quality Assurance in ML
The process of ensuring ML models meet expected performance standards.
Types of ML Quality Assurance
- Data Quality Checks - Verifies dataset consistency.
- Model Validation - Ensures model accuracy.
Example
Used in ensuring fairness and accuracy in ML models.
Quantization in Neural Networks
A technique to reduce model size and computational requirements.
Types of Quantization
- Post-Training Quantization - Applied after training.
- Quantization-Aware Training - Applied during training.
Example
Used in deploying deep learning models on edge devices.
Queueing Theory in ML
The study of waiting lines and service processes in ML-based systems.
Types of Queueing Models
- Markovian Queues - Based on memoryless properties.
- Non-Markovian Queues - More complex queueing systems.
Example
Used in optimizing cloud computing resources.
Query Optimization
The process of improving query performance in databases and search engines.
Types of Query Optimization
- Rule-Based Optimization - Uses predefined rules.
- Cost-Based Optimization - Selects queries based on execution cost.
Example
Used in SQL databases to improve query performance.
Quadratic Loss Function
A loss function that penalizes large prediction errors quadratically.
Types of Quadratic Loss Functions
- Mean Squared Error (MSE) - The most common form.
- Root Mean Squared Error (RMSE) - A variant taking the square root.
Example
Used in regression models to minimize prediction errors.
Quantitative Feature Selection
The process of selecting numerical features that impact a machine learning model.
Types of Quantitative Feature Selection
- Variance Threshold - Removes low-variance features.
- Mutual Information - Measures feature relevance.
Example
Used in pre-processing datasets before training.
Quantum Annealing
An optimization method that uses quantum effects to solve complex problems.
Types of Quantum Annealing
- Adiabatic Quantum Computation - Slowly evolves a quantum system.
- Hybrid Quantum Annealing - Combines classical and quantum methods.
Example
Used in optimization tasks for logistics and scheduling.
Quickprop Algorithm
An improved backpropagation method that accelerates learning in neural networks.
Types of Quickprop Variants
- Classic Quickprop - Uses second-order information.
- Adaptive Quickprop - Dynamically adjusts learning rates.
Example
Used in training deep neural networks efficiently.
Query-Based Sampling
A technique in active learning where models query uncertain samples for labeling.
Types of Query-Based Sampling
- Uncertainty Sampling - Selects samples with the highest uncertainty.
- Diversity Sampling - Ensures diverse sample selection.
Example
Used in reducing annotation costs in supervised learning.
Quasi-Convex Optimization
A mathematical optimization technique for quasi-convex functions.
Types of Quasi-Convex Optimization
- Gradient-Based Methods - Uses gradients for optimization.
- Subgradient Methods - Used for non-differentiable functions.
Example
Applied in constrained ML model optimization.
Quantum Entanglement in ML
A concept in quantum computing where qubits share information instantaneously.
Types of Quantum Entanglement
- Bell States - The simplest form of entanglement.
- GHZ States - More complex multi-qubit entanglements.
Example
Used in quantum-enhanced ML for secure communications.
Quantized Neural Networks
A deep learning model where weights and activations are quantized to reduce complexity.
Types of Quantized Neural Networks
- Binary Neural Networks - Use only 0s and 1s.
- Fixed-Point Quantization - Uses low-precision arithmetic.
Example
Used for running ML models on low-power devices.
Types of Query Performance Prediction
- Pre-Retrieval Prediction - Estimates query effectiveness before searching.
- Post-Retrieval Prediction - Evaluates results after retrieval.
Example
Used in search engines to enhance ranking algorithms.
Quality Assurance in ML
The process of ensuring machine learning models meet performance and accuracy standards.
Types of Quality Assurance
- Data Quality Assurance - Ensuring dataset integrity.
- Model Validation - Checking model accuracy and performance.
Example
Used in AI-driven healthcare applications to prevent biased decisions.
Quadratic Programming
An optimization technique where the objective function is quadratic, and constraints are linear.
Types of Quadratic Programming
- Convex Quadratic Programming - Guarantees a single optimal solution.
- Non-Convex Quadratic Programming - May have multiple solutions.
Example
Used in support vector machines (SVM) for classification problems.
Quantile Regression
A statistical technique that estimates the conditional median or quantiles of a response variable.
Types of Quantile Regression
- Linear Quantile Regression - Uses linear models for quantile estimation.
- Non-Parametric Quantile Regression - Uses kernel methods for estimation.
Example
Used in financial risk management for value-at-risk (VaR) predictions.
Quantum Kernel Estimation
An approach in quantum machine learning that uses quantum computing to improve kernel methods.
Types of Quantum Kernel Estimation
- Feature Mapping-Based Quantum Kernels - Transform classical data into quantum space.
- Hybrid Quantum-Classical Kernels - Combines classical and quantum computations.
Example
Used in quantum-enhanced support vector machines (QSVMs).
Query Expansion
A technique in information retrieval that expands user queries to improve search accuracy.
Types of Query Expansion
- Relevance Feedback Expansion - Uses past queries for improvement.
- Thesaurus-Based Expansion - Uses synonyms for query refinement.
Example
Used in search engines like Google to refine search results.
Queue-Based Reinforcement Learning
A reinforcement learning approach where experiences are stored and replayed using a queue.
Types of Queue-Based RL
- First-In-First-Out (FIFO) Queue - Oldest experiences are used first.
- Priority Queue - High-impact experiences are replayed more frequently.
Example
Used in deep Q-learning for optimizing experience replay.
Quantum-Inspired Neural Networks
Neural networks designed with quantum principles but implemented on classical hardware.
Types of Quantum-Inspired NNs
- Quantum Boltzmann Machines - Inspired by quantum statistical mechanics.
- Quantum-Inspired Convolutional Networks - Apply quantum-like transformations.
Example
Used in financial forecasting for complex pattern recognition.
Quorum-Based Learning
A decentralized learning approach where a decision is reached based on the majority of models or agents.
Types of Quorum-Based Learning
- Consensus-Based Quorum Learning - Requires agreement among all members.
- Weighted Quorum Learning - Assigns different importance to different members.
Example
Used in decentralized federated learning systems.
Quadratic Discriminant Analysis (QDA)
A classification technique that extends Linear Discriminant Analysis (LDA) by allowing quadratic decision boundaries.
Types of QDA
- Regularized QDA - Introduces a regularization term to prevent overfitting.
- Bayesian QDA - Uses probabilistic assumptions for classification.
Example
Used in image classification when data distribution is non-linear.
Quantization in ML
The process of reducing numerical precision in ML models to save computation power.
Types of Quantization
- Post-Training Quantization - Applied after training a model.
- Training-Aware Quantization - Integrates quantization during model training.
Example
Used in mobile AI models to reduce memory footprint.
Quasi-Newton Methods
A class of optimization algorithms that approximate the Hessian matrix to accelerate convergence in training machine learning models.
Types of Quasi-Newton Methods
- BFGS (Broyden–Fletcher–Goldfarb–Shanno) - A widely used method in optimization.
- L-BFGS (Limited-memory BFGS) - Optimized for large-scale problems.
Example
Used in deep learning for optimizing neural networks efficiently.
Query Learning
A machine learning approach where an algorithm actively selects data points to improve learning.
Types of Query Learning
- Membership Query Learning - The model queries unlabeled data for labels.
- Stream-Based Query Learning - Queries the most informative instances dynamically.
Example
Used in active learning for reducing labeled data requirements.
Quantum Reinforcement Learning
An approach combining reinforcement learning with quantum computing to improve decision-making.
Types of Quantum RL
- Quantum Policy Learning - Uses quantum circuits for policy optimization.
- Quantum Q-Learning - Applies quantum computation to improve Q-value estimations.
Example
Used in robotics to accelerate complex decision-making tasks.
Quasi-Random Sampling
A sampling method that improves uniformity over purely random selection.
Types of Quasi-Random Sampling
- Halton Sequence - A low-discrepancy sequence used in Monte Carlo simulations.
- Hammersley Sequence - Another method for improved coverage.
Example
Used in Bayesian optimization to select diverse training samples.
Query Optimization in ML
The process of optimizing data queries for better efficiency in ML workflows.
Types of Query Optimization
- Index-Based Query Optimization - Uses indexes to speed up searches.
- Cost-Based Query Optimization - Estimates computational costs for optimal execution.
Example
Used in big data frameworks like Spark for efficient ML model training.
Quantum Support Vector Machines
An extension of SVMs that leverage quantum kernels for better classification performance.
Types of Quantum SVMs
- Hybrid Quantum SVM - Uses classical optimization with quantum kernels.
- Fully Quantum SVM - Implements the entire process on quantum hardware.
Example
Used in quantum computing research for improving classification models.
Quadrature Methods in ML
Numerical integration techniques used for estimating probability distributions.
Types of Quadrature Methods
- Gaussian Quadrature - Uses specific weighting for higher accuracy.
- Monte Carlo Quadrature - Uses random sampling for integration.
Example
Used in Bayesian inference for posterior probability estimation.
Quickprop Algorithm
A second-order optimization algorithm that accelerates training of neural networks.
Types of Quickprop Variants
- Standard Quickprop - Uses second-order derivative approximations.
- Adaptive Quickprop - Adjusts learning rates dynamically.
Example
Used in backpropagation training for deep neural networks.
Quantized Neural Networks
Neural networks optimized by reducing precision of numerical computations.
Types of Quantized Neural Networks
- Binary Neural Networks - Weights and activations are constrained to -1 and 1.
- Integer Quantized Networks - Uses integer arithmetic to improve efficiency.
Example
Used in mobile AI applications for reducing power consumption.
Query-Based Active Learning
An approach where the model selects specific data points to improve learning.
Types of Query-Based Active Learning
- Uncertainty Sampling - Selects data where the model is least confident.
- Diversity Sampling - Picks diverse samples to improve generalization.
Example
Used in medical AI to reduce the need for extensive labeled data.
Machine Learning (ML)
ML is a subset of AI that enables machines to learn patterns from data and make predictions or decisions without explicit programming.
Types of ML
- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning
Example
Spam detection in emails using classification models.
Deep Learning (DL)
DL is a subset of ML that uses artificial neural networks to process complex data and perform high-level computations.
Example
Image recognition in self-driving cars.
Generative AI (Gen AI)
Gen AI refers to AI models that generate new content, including text, images, and code, using trained knowledge bases.
Example
AI models like ChatGPT and Stable Diffusion that generate text and images.