Unsupervised Learning Algorithms
Expert-defined terms from the Professional Certificate in Artificial Intelligence for Welding Processes course at LearnUNI. Free to read, free to share, paired with a professional course.
Abstract Representation refers to the process of simplifying complex data into a… #
Related terms include Dimensionality Reduction, Feature Extraction, and Data Compression. Abstract Representation is essential in Unsupervised Learning Algorithms as it enables the discovery of hidden patterns and relationships within the data.
Accuracy is a measure of how close the predicted values are to the actual values… #
Related terms include Precision, Recall, F1-Score, and Mean Squared Error. In the context of Unsupervised Learning Algorithms, Accuracy is not always applicable, as there is no labeled data to compare with.
Activation Function is a mathematical function that introduces non #
linearity into a neural network, enabling the model to learn and represent more complex relationships between inputs and outputs. Related terms include Sigmoid, ReLU, and Tanh. Activation Functions play a crucial role in Unsupervised Learning Algorithms, such as Autoencoders and Generative Adversarial Networks.
Anomaly Detection is the process of identifying data points that deviate signifi… #
Related terms include Outlier Detection, Novelty Detection, and One-Class Classification. Anomaly Detection has numerous practical applications, including fraud detection, network security, and quality control.
Artificial Neural Network is a computational model inspired by the structure and… #
Related terms include Deep Learning, Convolutional Neural Networks, and Recurrent Neural Networks. Artificial Neural Networks are widely used in Unsupervised Learning Algorithms, such as dimensionality reduction and generative modeling.
Autoencoder is a type of neural network that learns to compress and recon… #
Related terms include Variational Autoencoder, Denoising Autoencoder, and Contractive Autoencoder. Autoencoders are essential in Unsupervised Learning Algorithms, as they enable the discovery of hidden patterns and relationships within the data.
Batch Normalization is a technique used to normalize the inputs of each layer in… #
Related terms include Layer Normalization, Instance Normalization, and Group Normalization. Batch Normalization is commonly used in Unsupervised Learning Algorithms, such as Generative Adversarial Networks and Autoencoders.
Clustering is the process of grouping similar data points into clusters, often u… #
Related terms include K-Means, Hierarchical Clustering, and Density-Based Clustering. Clustering has numerous practical applications, including customer segmentation, gene expression analysis, and image segmentation.
Convolutional Neural Network is a type of neural network designed to proc… #
Related terms include Recurrent Neural Networks, Fully Convolutional Networks, and U-Net. Convolutional Neural Networks are widely used in Unsupervised Learning Algorithms, such as image generation and image-to-image translation.
Data Augmentation is a technique used to increase the size and diversity of a da… #
Related terms include Data Generation, Data Simulation, and Transfer Learning. Data Augmentation is also applicable in Unsupervised Learning Algorithms, such as Generative Adversarial Networks and Variational Autoencoders.
Data Preprocessing is the process of cleaning, transforming, and preparing data… #
Related terms include Data Cleaning, Data Transformation, and Feature Engineering. Data Preprocessing is essential in Unsupervised Learning Algorithms, as it enables the discovery of hidden patterns and relationships within the data.
Deep Learning is a subfield of machine learning that focuses on the use o… #
Related terms include Convolutional Neural Networks, Recurrent Neural Networks, and Autoencoders. Deep Learning is widely used in Unsupervised Learning Algorithms, such as dimensionality reduction and generative modeling.
Density #
Based Clustering is a type of clustering algorithm that groups data points into clusters based on their density and proximity to each other, often used in unsupervised learning algorithms to discover hidden patterns and relationships. Related terms include DBSCAN, OPTICS, and HDBSCAN. Density-Based Clustering has numerous practical applications, including data visualization, customer segmentation, and gene expression analysis.
Dimensionality Reduction is the process of reducing the number of features or di… #
Related terms include Principal Component Analysis, t-SNE, and Autoencoders. Dimensionality Reduction is essential in Unsupervised Learning Algorithms, as it enables the discovery of hidden patterns and relationships within the data.
Distance Metric is a mathematical function that measures the similarity or dissi… #
Related terms include Euclidean Distance, Manhattan Distance, and Cosine Similarity. Distance Metrics are widely used in Unsupervised Learning Algorithms, such as clustering and dimensionality reduction.
Expectation #
Maximization Algorithm is a statistical algorithm used to estimate the parameters of a model when the data is incomplete or missing, often used in unsupervised learning algorithms to discover hidden patterns and relationships. Related terms include Gaussian Mixture Models, Hidden Markov Models, and Variational Inference. Expectation-Maximization Algorithm has numerous practical applications, including data imputation, clustering, and density estimation.
Feature Engineering is the process of selecting, constructing, and transforming… #
Related terms include Feature Selection, Feature Construction, and Feature Learning. Feature Engineering is essential in Unsupervised Learning Algorithms, as it enables the discovery of hidden patterns and relationships within the data.
Feature Extraction is the process of extracting relevant features or variables f… #
Related terms include Feature Engineering, Feature Selection, and Dimensionality Reduction. Feature Extraction is widely used in Unsupervised Learning Algorithms, such as clustering and dimensionality reduction.
Generative Adversarial Network is a type of deep learning model that lear… #
Related terms include Variational Autoencoder, Generative Model, and Adversarial Training. Generative Adversarial Networks are essential in Unsupervised Learning Algorithms, as they enable the discovery of hidden patterns and relationships within the data.
Gaussian Mixture Model is a statistical model that represents a probabili… #
Related terms include Expectation-Maximization Algorithm, Hidden Markov Models, and Variational Inference. Gaussian Mixture Models have numerous practical applications, including clustering, density estimation, and anomaly detection.
Gradient Descent is an optimization algorithm used to minimize the loss f… #
Related terms include Gradient Ascent, Stochastic Gradient Descent, and Gradient Descent with Momentum. Gradient Descent is widely used in Unsupervised Learning Algorithms, such as dimensionality reduction and generative modeling.
Hierarchical Clustering is a type of clustering algorithm that groups data point… #
Related terms include K-Means, Density-Based Clustering, and Agglomerative Clustering. Hierarchical Clustering has numerous practical applications, including data visualization, customer segmentation, and gene expression analysis.
K-Means is a type of clustering algorithm that groups data points into K cluster… #
Related terms include Hierarchical Clustering, Density-Based Clustering, and Expectation-Maximization Algorithm. K-Means has numerous practical applications, including customer segmentation, gene expression analysis, and image segmentation.
K-Nearest Neighbors is a machine learning algorithm that predicts the lab… #
K-Nearest Neighbors is widely used in Unsupervised Learning Algorithms, such as clustering and dimensionality reduction.
Latent Variable is a statistical variable that is not directly observed b… #
Related terms include Latent Factor Analysis, Latent Dirichlet Allocation, and Variational Autoencoder. Latent Variables are essential in Unsupervised Learning Algorithms, as they enable the discovery of hidden patterns and relationships within the data.
Local Outlier Factor is a measure of the degree to which a data point is an outl… #
Related terms include Anomaly Detection, Outlier Detection, and One-Class Classification. Local Outlier Factor has numerous practical applications, including fraud detection, network security, and quality control.
Mean Squared Error is a measure of the average squared difference between predic… #
Related terms include Mean Absolute Error, Root Mean Squared Error, and Coefficient of Determination. Mean Squared Error is widely used in Unsupervised Learning Algorithms, such as dimensionality reduction and generative modeling.
Neural Network is a computational model inspired by the structure and function o… #
Neural Networks are widely used in Unsupervised Learning Algorithms, such as dimensionality reduction and generative modeling.
Non #
Negative Matrix Factorization is a dimensionality reduction technique that factorizes a matrix into two non-negative matrices, often used in unsupervised learning algorithms to discover hidden patterns and relationships. Related terms include Singular Value Decomposition, Principal Component Analysis, and Independent Component Analysis. Non-Negative Matrix Factorization has numerous practical applications, including data visualization, customer segmentation, and gene expression analysis.
One #
Class Classification is a type of machine learning algorithm that trains a model to recognize a single class of data, often used in unsupervised learning algorithms to detect anomalies or outliers. Related terms include Anomaly Detection, Outlier Detection, and Local Outlier Factor. One-Class Classification has numerous practical applications, including fraud detection, network security, and quality control.
Outlier Detection is the process of identifying data points that deviate signifi… #
Related terms include Anomaly Detection, Novelty Detection, and One-Class Classification. Outlier Detection has numerous practical applications, including fraud detection, network security, and quality control.
Overfitting is the phenomenon where a machine learning model performs wel… #
Related terms include Underfitting, Regularization, and Cross-Validation. Overfitting is a common challenge in Unsupervised Learning Algorithms, and techniques such as regularization and early stopping can help prevent it.
Principal Component Analysis is a dimensionality reduction technique that… #
Related terms include Singular Value Decomposition, Non-Negative Matrix Factorization, and Independent Component Analysis. Principal Component Analysis has numerous practical applications, including data visualization, customer segmentation, and gene expression analysis.
Recurrent Neural Network is a type of neural network designed to process… #
Related terms include Convolutional Neural Networks, Long Short-Term Memory, and Gated Recurrent Unit. Recurrent Neural Networks are widely used in Unsupervised Learning Algorithms, such as language modeling and text generation.
Regularization is a technique used to prevent overfitting in machine</… #
Related terms include L1 Regularization, L2 Regularization, and Early Stopping. Regularization is essential in Unsupervised Learning Algorithms, as it enables the discovery of hidden patterns and relationships within the data.
Self #
Organizing Map is a type of neural network that projects high-dimensional data onto a lower-dimensional space, often used in unsupervised learning algorithms to discover hidden patterns and relationships. Self-Organizing Maps have numerous practical applications, including data visualization, customer segmentation, and gene expression analysis.
Singular Value Decomposition is a dimensionality reduction technique that… #
Related terms include Principal Component Analysis, Non-Negative Matrix Factorization, and Independent Component Analysis. Singular Value Decomposition has numerous practical applications, including data visualization, customer segmentation, and gene expression analysis.
T-SNE is a dimensionality reduction technique that maps high-dimensional… #
Related terms include Principal Component Analysis, Autoencoders, and Self-Organizing Maps. T-SNE has numerous practical applications, including data visualization, customer segmentation, and gene expression analysis.
Underfitting is the phenomenon where a machine learning model is too simp… #
Related terms include Overfitting, Regularization, and Cross-Validation. Underfitting is a common challenge in Unsupervised Learning Algorithms, and techniques such as feature engineering and model selection can help prevent it.
Unsupervised Learning is a type of machine learning that involves trainin… #
Related terms include Supervised Learning, Semi-Supervised Learning, and Reinforcement Learning. Unsupervised Learning is essential in many applications, including data visualization, customer segmentation, and gene expression analysis.
Variational Autoencoder is a type of deep learning model that learns to c… #
Related terms include Autoencoder, Generative Adversarial Network, and Latent Variable Model. Variational Autoencoders are essential in Unsupervised Learning Algorithms, as they enable the discovery of hidden patterns and relationships within the data.
Vector Quantization is a technique used to reduce the dimensionality of a datase… #
Vector Quantization has numerous practical applications, including data visualization, customer segmentation, and gene expression analysis.