PAC Learning (Probably Approximately Correct Learning)
A theoretical framework that defines how efficiently a model can learn a function from a limited number of samples.
Types of PAC Learning
- Strong PAC Learning - Achieves low error with high probability.
- Weak PAC Learning - Achieves slightly better than random guessing.
Example
Used in computational learning theory to analyze machine learning algorithms.
Pairwise Learning
A learning approach where the model considers pairs of data points instead of individual instances.
Types of Pairwise Learning
- Pairwise Classification - Distinguishes between two classes at a time.
- Pairwise Ranking - Compares pairs to determine their relative order.
Example
Used in recommendation systems and ranking algorithms like RankNet.
Parallel Learning
A technique where multiple models or computations run simultaneously to speed up learning.
Types of Parallel Learning
- Data Parallelism - Distributes data across multiple machines.
- Model Parallelism - Splits a model across multiple processors.
Example
Used in deep learning frameworks like TensorFlow for distributed training.
Parameter Sharing
A technique in deep learning where certain parameters are reused across multiple parts of a model.
Types of Parameter Sharing
- Convolutional Parameter Sharing - Filters shared across different regions.
- Recurrent Parameter Sharing - Weights reused across time steps.
Example
Used in CNNs and RNNs to reduce the number of parameters.
Partial Least Squares (PLS)
A regression technique that reduces dimensionality while maintaining correlations between input and output variables.
Types of PLS
- PLS Regression - Used for predictive modeling.
- PLS Discriminant Analysis - Used for classification tasks.
Example
Used in chemometrics and financial modeling.
Partially Observable Markov Decision Process (POMDP)
A generalization of Markov Decision Processes where the agent does not have full observability of the state space.
Types of Decision Processes
- MDP (Markov Decision Process) - Fully observable states.
- POMDP - Partially observable states.
Example
Used in robotics and autonomous driving.
Patch-Based Learning
A technique where models learn from small patches of data rather than entire samples.
Types of Patch-Based Learning
- Image Patches - Used in CNNs for feature extraction.
- Text Patches - Used in transformers like Vision Transformers (ViTs).
Example
Used in computer vision and NLP models.
Pattern Recognition
A machine learning approach that focuses on identifying patterns in data.
Types of Pattern Recognition
- Supervised Pattern Recognition - Uses labeled data.
- Unsupervised Pattern Recognition - Finds patterns without labels.
Example
Used in facial recognition and speech recognition systems.
Perceptron Algorithm
A simple neural network model used for binary classification.
Types of Perceptron
- Single-Layer Perceptron - Can learn linearly separable functions.
- Multi-Layer Perceptron (MLP) - Can learn complex patterns.
Example
Used in early neural networks for classifying handwritten digits.
Permutation Importance
A technique to measure feature importance by shuffling feature values and observing the impact on model performance.
Types of Feature Importance
- Model-Based Importance - Uses learned parameters.
- Permutation Importance - Measures impact by altering feature values.
Example
Used in feature selection for tree-based models like Random Forests.
Phi Coefficient
A measure of association between two binary variables, similar to correlation.
Types of Correlation Measures
- Pearson Correlation - Measures linear relationships.
- Phi Coefficient - Used for binary variables.
Example
Used in classification problems to evaluate feature relationships.
Policy Gradient Methods
A reinforcement learning approach where policies are directly optimized using gradient-based methods.
Types of Policy Gradient Methods
- Vanilla Policy Gradient (VPG) - Directly optimizes the policy.
- Proximal Policy Optimization (PPO) - More stable and efficient.
Example
Used in training agents in OpenAI Gym environments.
Polynomial Regression
A type of regression where the relationship between input and output is modeled as an nth-degree polynomial.
Types of Polynomial Regression
- Linear Polynomial Regression - Degree 1 polynomial.
- Higher-Order Polynomial Regression - Degree 2 or more.
Example
Used in curve fitting and predictive modeling.
Pooling Layers
A technique in convolutional neural networks (CNNs) that reduces spatial dimensions while preserving important features.
Types of Pooling
- Max Pooling - Selects the maximum value from a region.
- Average Pooling - Computes the average value from a region.
Example
Used in image recognition tasks in deep learning.
Principal Component Analysis (PCA)
A dimensionality reduction technique that transforms features into a set of orthogonal components.
Types of PCA
- Linear PCA - Uses eigenvalue decomposition.
- Kernel PCA - Uses kernel functions for non-linear data.
Example
Used in image compression and feature extraction.
Probabilistic Graphical Models (PGMs)
Graph-based models that represent probabilistic relationships between variables.
Types of PGMs
- Bayesian Networks - Directed graphical models.
- Markov Random Fields - Undirected graphical models.
Example
Used in spam filtering and medical diagnosis.
Probability Calibration
A technique to adjust the predicted probabilities of a classifier to better reflect true likelihoods.
Types of Probability Calibration
- Platt Scaling - Logistic regression-based adjustment.
- Isotonic Regression - Non-parametric calibration method.
Example
Used in medical diagnosis models for reliable probability estimates.
Propensity Score Matching (PSM)
A statistical matching technique used to reduce selection bias in observational studies.
Types of Propensity Matching
- Nearest Neighbor Matching - Matches similar observations.
- Kernel Matching - Uses weighted matching.
Example
Used in causal inference studies in economics and healthcare.
Pruning in Neural Networks
A technique to remove unnecessary weights in a neural network to improve efficiency.
Types of Pruning
- Magnitude-Based Pruning - Removes smallest weights.
- Structured Pruning - Removes entire neurons or layers.
Example
Used in mobile deep learning models to reduce size and computation.
Pseudoinverse in Machine Learning
The Moore-Penrose pseudoinverse is used to solve linear equations and optimize models.
Types of Matrix Inverses
- Regular Inverse - Exists for square matrices.
- Moore-Penrose Pseudoinverse - Works for non-square matrices.
Example
Used in solving least-squares problems in linear regression.
Q-Learning
A model-free reinforcement learning algorithm that learns the value of an action in a given state.
Types of Q-Learning
- Tabular Q-Learning - Stores Q-values in a table.
- Deep Q-Learning (DQN) - Uses deep neural networks for function approximation.
Example
Used in game-playing AI like DeepMind's AlphaGo.
Quadratic Programming
An optimization technique where the objective function is quadratic and constraints are linear.
Types of Quadratic Programming
- Convex Quadratic Programming - Ensures a single optimal solution.
- Non-Convex Quadratic Programming - May have multiple solutions.
Example
Used in support vector machines (SVMs) for margin optimization.
Quantum Machine Learning
An emerging field that combines quantum computing with machine learning to enhance computational efficiency.
Types of Quantum ML
- Quantum Support Vector Machines - Uses quantum kernels.
- Quantum Neural Networks - Quantum-enhanced deep learning models.
Example
Used in drug discovery and materials science.
Query Expansion
A technique in information retrieval that enhances search queries by adding related terms.
Types of Query Expansion
- Automatic Query Expansion - Uses machine learning to refine queries.
- Manual Query Expansion - Users refine searches based on suggestions.
Example
Used in search engines like Google to improve results.
Queueing Theory in ML
A mathematical approach to managing waiting lines and resource allocation in machine learning systems.
Types of Queueing Models
- Markovian Queueing Models - Based on probabilistic state transitions.
- Non-Markovian Queueing Models - Complex arrival and service distributions.
Example
Used in optimizing cloud computing and data center management.
Quorum Sensing in AI
A bio-inspired approach where multiple AI agents coordinate decisions based on shared thresholds.
Types of Quorum Sensing
- Local Quorum Sensing - Decisions based on nearby agents.
- Global Quorum Sensing - Decisions based on all agents.
Example
Used in swarm intelligence and robotic coordination.
Quickprop Algorithm
An optimization algorithm that accelerates backpropagation by using second-order information.
Types of Gradient-Based Optimization
- Backpropagation - Standard weight update approach.
- Quickprop - Faster convergence using second derivatives.
Example
Used in training neural networks for faster convergence.
Quincunx Sampling
A technique used in machine learning to sample data points in a structured manner.
Types of Sampling
- Random Sampling - Unbiased selection of data points.
- Quincunx Sampling - Grid-based structured sampling.
Example
Used in image processing and statistical modeling.
Quotient Feature Space
A mathematical transformation where similar features are grouped to reduce dimensionality.
Types of Feature Transformations
- Principal Component Analysis (PCA) - Linear feature reduction.
- Quotient Feature Space - Groups similar features.
Example
Used in clustering and dimensionality reduction.
Quadrature Methods
Numerical integration techniques used to approximate functions in machine learning.
Types of Quadrature Methods
- Gaussian Quadrature - High accuracy integration.
- Monte Carlo Integration - Stochastic approximation.
Example
Used in Bayesian inference and probabilistic modeling.
Probabilistic Graphical Models (PGMs)
A framework that represents probability distributions using graphs.
Types of PGMs
- Bayesian Networks - Directed graphical models.
- Markov Random Fields - Undirected graphical models.
Example
Used in speech recognition and medical diagnosis.
Probabilistic Latent Semantic Analysis (PLSA)
A statistical technique used for analyzing word-document relationships.
Types of Semantic Analysis
- Latent Semantic Analysis (LSA) - Uses singular value decomposition.
- Probabilistic LSA - Uses probabilistic models.
Example
Used in document clustering and recommendation systems.
Probabilistic Neural Networks (PNN)
A type of neural network that applies Bayes' rule for classification.
Types of Neural Networks
- Feedforward Neural Networks - Simple forward-propagation models.
- Probabilistic Neural Networks - Uses statistical decision-making.
Example
Used in medical diagnosis and handwriting recognition.
Proximal Policy Optimization (PPO)
A reinforcement learning algorithm that optimizes policies with stability and efficiency.
Types of Policy Optimization
- Trust Region Policy Optimization (TRPO) - Constrained optimization.
- Proximal Policy Optimization (PPO) - More stable updates.
Example
Used in training AI agents like OpenAI’s Dota 2 bot.
Pseudoinverse in Machine Learning
A generalized matrix inversion technique used in linear regression.
Types of Inverses
- Moore-Penrose Pseudoinverse - Standard method.
- Generalized Inverses - Used in specific cases.
Example
Used in solving linear least squares problems.
Python for Machine Learning
A popular programming language used for implementing machine learning models.
Types of ML Libraries in Python
- Scikit-learn - Classical ML algorithms.
- TensorFlow/PyTorch - Deep learning frameworks.
Example
Used for building classification and regression models.
Principal Component Analysis (PCA)
A dimensionality reduction technique that transforms correlated features into uncorrelated ones.
Types of PCA
- Linear PCA - Standard principal component extraction.
- Kernel PCA - Non-linear variant.
Example
Used in face recognition and data visualization.
Perceptron
A fundamental neural network model used for binary classification.
Types of Perceptrons
- Single-layer Perceptron - Can classify linearly separable data.
- Multi-layer Perceptron (MLP) - Used in deep learning.
Example
Used in image recognition and spam filtering.
Probabilistic Programming
A programming paradigm that integrates probability and inference into machine learning models.
Types of Probabilistic Programming Languages
- Pyro - Deep probabilistic programming.
- Stan - Bayesian modeling.
Example
Used in financial forecasting and AI reasoning.
Pooling in Neural Networks
A technique used in convolutional neural networks (CNNs) to reduce spatial dimensions.
Types of Pooling
- Max Pooling - Retains the highest value in a region.
- Average Pooling - Computes the average value.
Example
Used in CNNs for image classification.
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.