Kalman Filter
A recursive algorithm used for estimating the state of a dynamic system from noisy observations.
Types of Kalman Filters
- Linear Kalman Filter - Assumes linear system dynamics.
- Extended Kalman Filter (EKF) - Handles nonlinear systems.
- Unscented Kalman Filter (UKF) - Uses sigma points for better estimation.
Example
Used in self-driving cars for sensor fusion and localization.
K-Anonymity
A privacy-preserving technique ensuring that each individual is indistinguishable from at least k-1 others in a dataset.
Types of K-Anonymity Methods
- Generalization - Replacing specific values with broader categories.
- Suppression - Removing parts of data to ensure anonymity.
Example
Used in healthcare data to anonymize patient records.
K-Fold Cross-Validation
A technique for assessing model performance by splitting data into k subsets and training on k-1 while testing on the remaining one.
Types of K-Fold Cross-Validation
- Stratified K-Fold - Preserves class distribution.
- Repeated K-Fold - Runs multiple cross-validation iterations.
Example
Used in machine learning to evaluate model generalization.
K-Means Clustering
An unsupervised learning algorithm that partitions data into k clusters by minimizing intra-cluster variance.
Types of K-Means Variants
- Mini-Batch K-Means - Uses small data batches for efficiency.
- K-Means++ - Improves initial centroid selection.
Example
Used in customer segmentation for targeted marketing.
K-Medoids Clustering
A clustering algorithm similar to K-Means but uses actual data points as cluster centers (medoids) to reduce sensitivity to outliers.
Types of K-Medoids Methods
- PAM (Partitioning Around Medoids) - Classic implementation.
- CLARA - Scales K-Medoids for large datasets.
Example
Used in fraud detection to group similar suspicious transactions.
K-Nearest Neighbors (KNN)
A non-parametric supervised learning algorithm that classifies data points based on the majority vote of their k nearest neighbors.
Types of KNN
- Weighted KNN - Assigns more weight to closer neighbors.
- KNN Regression - Uses neighbors' values for prediction.
Example
Used in recommendation systems for product suggestions.
K-Shortest Paths Algorithm
A graph-based algorithm that finds the k shortest paths between two nodes.
Types of K-Shortest Paths Algorithms
- K-Simple Paths - No repeating nodes.
- K-Loop Paths - Allows cycles in the path.
Example
Used in network routing for optimal packet transmission.
Kernel Density Estimation (KDE)
A non-parametric method to estimate the probability density function of a dataset.
Types of KDE Kernels
- Gaussian Kernel - Smooth bell-shaped density.
- Epanechnikov Kernel - More efficient for density estimation.
Example
Used in anomaly detection for fraud detection.
Kernel Principal Component Analysis (KPCA)
A nonlinear extension of PCA that uses kernel functions to capture complex data relationships.
Types of Kernel Functions
- Radial Basis Function (RBF) Kernel - Captures nonlinear structures.
- Polynomial Kernel - Captures polynomial relationships.
Example
Used in face recognition to reduce high-dimensional data.
Kernel Ridge Regression
A regression technique that combines ridge regression with kernel methods to handle non-linearity.
Types of Kernel Ridge Regression
- Linear Kernel Ridge Regression - Regularized linear model.
- Nonlinear Kernel Ridge Regression - Uses RBF or polynomial kernels.
Example
Used in financial forecasting for stock price prediction.
Kernel Smoothing
A technique used to smooth noisy data by applying a weighted average with a kernel function.
Types of Kernel Smoothing
- Gaussian Kernel Smoothing - Uses a Gaussian function for smoothing.
- Epanechnikov Kernel Smoothing - More efficient in density estimation.
Example
Used in time series forecasting to remove noise.
Kernel Support Vector Machines (Kernel SVM)
An extension of SVM that applies kernel tricks to classify non-linearly separable data.
Types of Kernel SVM
- Radial Basis Function (RBF) Kernel - Captures complex patterns.
- Polynomial Kernel - Maps data into higher dimensions.
Example
Used in handwriting recognition to classify characters.
Kernel Trick
A mathematical technique that transforms non-linearly separable data into a higher-dimensional space where it becomes linearly separable.
Types of Kernel Functions
- Linear Kernel - No transformation applied.
- Sigmoid Kernel - Mimics neural network activation.
Example
Used in SVM to classify complex datasets.
Types of Keyphrase Extraction
- Statistical Methods - Uses frequency-based algorithms.
- Machine Learning Methods - Uses supervised learning models.
Example
Used in search engine optimization (SEO) to extract keywords from documents.
Kinetic Learning
A machine learning approach that involves movement-based or dynamic data for training models.
Types of Kinetic Learning Applications
- Gesture Recognition - Identifying hand and body movements.
- Motion Prediction - Predicting movement trajectories.
Example
Used in gaming for motion-controlled interactions.
Knowledge Acquisition
The process of collecting and structuring knowledge from various sources for use in AI models.
Types of Knowledge Acquisition
- Manual Knowledge Acquisition - Experts input data.
- Automated Knowledge Acquisition - AI learns from data.
Example
Used in expert systems to develop rule-based AI models.
Knowledge-Based Systems
AI systems that use structured knowledge to make decisions and solve problems.
Types of Knowledge-Based Systems
- Rule-Based Systems - Uses if-then rules.
- Case-Based Systems - Uses past cases to make decisions.
Example
Used in medical diagnosis systems.
Knowledge Distillation
A model compression technique where a smaller model (student) learns from a larger model (teacher).
Types of Knowledge Distillation
- Soft Target Distillation - Transfers probability distributions.
- Feature-Based Distillation - Transfers internal feature maps.
Example
Used to compress large transformer models like BERT.
Knowledge Graph
A structured representation of knowledge that connects entities and relationships.
Types of Knowledge Graphs
- Ontology-Based Knowledge Graph - Uses predefined schemas.
- Open Knowledge Graph - Dynamically generated from data.
Example
Used in search engines to improve query understanding.
Knowledge Representation
The method of encoding information in AI systems for reasoning and inference.
Types of Knowledge Representation
- Symbolic Representation - Uses logic and rules.
- Subsymbolic Representation - Uses neural networks.
Example
Used in chatbots to store structured information.
Knowledge Transfer
The process of transferring knowledge from one machine learning model to another to improve learning efficiency.
Types of Knowledge Transfer
- Homogeneous Transfer - Knowledge transfer between similar domains.
- Heterogeneous Transfer - Knowledge transfer across different domains.
Example
Used in transfer learning where a pre-trained model like ResNet is adapted for a new task.
Kullback-Leibler Divergence (KL Divergence)
A statistical measure that quantifies how one probability distribution differs from another.
Types of KL Divergence
- Forward KL Divergence - Measures divergence from true distribution to approximation.
- Reverse KL Divergence - Measures divergence from approximation to true distribution.
Example
Used in Variational Autoencoders (VAEs) to measure the similarity between distributions.
Kalman Filter
An algorithm used to estimate the state of a system in the presence of noise.
Types of Kalman Filters
- Linear Kalman Filter - Assumes linear system dynamics.
- Extended Kalman Filter - Used for non-linear systems.
Example
Used in robotics for sensor fusion and motion tracking.
Kernel Approximation
A technique used to approximate kernel functions in machine learning to reduce computational cost.
Types of Kernel Approximation
- Random Fourier Features - Approximates kernels using Fourier transforms.
- Nyström Method - Approximates kernels using a subset of data.
Example
Used in large-scale SVMs to make training more efficient.
Kernel Density Estimation (KDE)
A non-parametric way to estimate the probability density function of a dataset.
Types of Kernel Density Estimation
- Gaussian KDE - Uses a Gaussian kernel.
- Epanechnikov KDE - Uses an Epanechnikov kernel.
Example
Used in anomaly detection to estimate the density of normal data points.
K-fold Cross-Validation
A model validation technique where the dataset is split into K subsets to train and test the model multiple times.
Types of K-fold Cross-Validation
- Stratified K-fold - Maintains class distribution in folds.
- Repeated K-fold - Runs K-fold multiple times for stability.
Example
Used in model evaluation to reduce overfitting and improve generalization.
K-means Clustering
An unsupervised learning algorithm that partitions data into K clusters.
Types of K-means Variations
- Standard K-means - Uses Euclidean distance.
- K-medoids - Uses a central data point instead of centroids.
Example
Used in customer segmentation for marketing strategies.
K-medoids Clustering
A clustering algorithm similar to K-means but uses medoids instead of centroids for robustness.
Types of K-medoids Algorithms
- PAM (Partitioning Around Medoids) - Traditional K-medoids approach.
- CLARA (Clustering Large Applications) - Scales K-medoids for large datasets.
Example
Used in fraud detection where outliers need to be minimized.
Kernel Principal Component Analysis (Kernel PCA)
A non-linear extension of PCA that applies kernel functions to capture complex patterns.
Types of Kernel PCA
- Polynomial Kernel PCA - Maps data to a polynomial feature space.
- RBF Kernel PCA - Uses a Gaussian kernel for transformation.
Example
Used in image recognition to reduce dimensionality while preserving non-linear relationships.
Kohonen Self-Organizing Maps (SOM)
An unsupervised neural network model that organizes high-dimensional data into a low-dimensional grid.
Types of Kohonen SOM
- Rectangular Grid SOM - Uses a rectangular topology.
- Hexagonal Grid SOM - Uses a hexagonal topology for better neighborhood relationships.
Example
Used in pattern recognition and feature visualization.
Kriging
A geostatistical method used for interpolation and spatial data prediction.
Types of Kriging
- Ordinary Kriging - Assumes a constant but unknown mean.
- Universal Kriging - Accounts for known trends in data.
Example
Used in meteorology for weather prediction and mining for resource estimation.
Kronecker Product
A mathematical operation on two matrices that produces a block matrix.
Types of Applications
- Covariance Estimation - Used in Gaussian Processes.
- Neural Networks - Efficient weight representations in deep learning.
Example
Used in tensor decomposition for model compression.
K-Sparse Autoencoder
A type of autoencoder that enforces sparsity by keeping only the top K activations.
Types of K-Sparse Autoencoders
- Static K-Sparse - Fixed number of active neurons.
- Dynamic K-Sparse - Adaptive sparsity based on input.
Example
Used in feature learning for high-dimensional datasets.
K-test
A statistical test used to determine the significance of clustering structures in data.
Types of K-tests
- Parametric K-test - Assumes normal distribution of clusters.
- Non-parametric K-test - Makes fewer assumptions about data distribution.
Example
Used to validate K-means clustering results.
K-step Lookahead
A technique used in reinforcement learning to make decisions based on future rewards.
Types of K-step Lookahead
- Fixed K-step - Uses a constant number of future steps.
- Adaptive K-step - Dynamically adjusts based on uncertainty.
Example
Used in Monte Carlo Tree Search for game-playing AI.
Knowledge Distillation
A technique where a smaller model learns from a larger, pre-trained model.
Types of Knowledge Distillation
- Soft Distillation - Uses softened probabilities for learning.
- Hard Distillation - Uses exact class labels.
Example
Used in model compression for deploying deep learning models on edge devices.
Kernel Ridge Regression (KRR)
A regression algorithm that combines ridge regression with kernel methods.
Types of Kernel Ridge Regression
- Linear KRR - Uses a linear kernel.
- Gaussian KRR - Uses a radial basis function (RBF) kernel.
Example
Used in stock market prediction and financial modeling.
K-Nearest Neighbor Imputation (KNN Imputation)
A technique for handling missing data by using the K nearest neighbors to predict missing values.
Types of KNN Imputation
- Mean-based KNN - Uses the mean of neighbors for imputation.
- Mode-based KNN - Uses the most frequent value among neighbors.
Example
Used in healthcare datasets for handling missing patient records.
Knowledge Graphs
A structured representation of knowledge using entities and their relationships.
Types of Knowledge Graphs
- Open Knowledge Graphs - Publicly available (e.g., Google Knowledge Graph).
- Enterprise Knowledge Graphs - Private, used for business intelligence.
Example
Used in search engines to enhance query understanding.
K-Order Markov Model
A probabilistic model that considers K previous states to predict the next state.
Types of K-Order Markov Models
- First-Order Markov Model - Considers only the previous state.
- Higher-Order Markov Model - Uses multiple past states.
Example
Used in speech recognition and text prediction applications.
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.