A-Z of Machine Learning and Computer Vision Terms

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A
A
AI Agent
AI Agent
AI Assistants
AI Assistants
AI Assisted Labeling
AI Assisted Labeling
Active Learning
Active Learning
Algorithm
Algorithm
Anchor Boxes
Anchor Boxes
Anomaly Detection
Anomaly Detection
Artificial Intelligence (AI)
Artificial Intelligence (AI)
Attribute
Attribute
B
B
Backpropagation
Backpropagation
Bagging
Bagging
Batch
Batch
Batch Normalization
Batch Normalization
Bayesian Network
Bayesian Network
Bias
Bias
Big Data
Big Data
Binary Classification
Binary Classification
Blur
Blur
Boosting
Boosting
Bounding Box
Bounding Box
C
C
COCO
COCO
Calibration
Calibration
Calibration Curve
Calibration Curve
Canonical Correlation Analysis (CCA)
Canonical Correlation Analysis (CCA)
Case-Based Reasoning
Case-Based Reasoning
Chain of Thought (CoT)
Chain of Thought (CoT)
ChatGPT
ChatGPT
Chi-Squared Automatic Interaction Detection (CHAID)
Chi-Squared Automatic Interaction Detection (CHAID)
Class Boundary
Class Boundary
Class Boundary (Statistics & Machine Learning)
Class Boundary (Statistics & Machine Learning)
Class Imbalance
Class Imbalance
Clustering
Clustering
Collaborative Filtering
Collaborative Filtering
Computer Vision
Computer Vision
Computer Vision Model
Computer Vision Model
Concept Drift
Concept Drift
Conditional Random Field (CRF)
Conditional Random Field (CRF)
Confusion Matrix
Confusion Matrix
Constrained Clustering
Constrained Clustering
Contrastive Learning
Contrastive Learning
Convolutional Neural Network (CNN)
Convolutional Neural Network (CNN)
Convolutional Neural Networks (CNNs)
Convolutional Neural Networks (CNNs)
Cross-Validation
Cross-Validation
D
D
DICOM
DICOM
Data Approximation
Data Approximation
Data Augmentation
Data Augmentation
Data Drift
Data Drift
Data Error
Data Error
Data Mining
Data Mining
Data Operations
Data Operations
Data Pre-processing
Data Pre-processing
Data Quality
Data Quality
Dataset
Dataset
Decision Boundary
Decision Boundary
Decision List
Decision List
Decision Stump
Decision Stump
Decision Tree
Decision Tree
Deep Learning
Deep Learning
Deep Neural Networks
Deep Neural Networks
Dimensionality Reduction
Dimensionality Reduction
Dropout
Dropout
Dynamic and Event-Based Classifications
Dynamic and Event-Based Classifications
E
E
Edge Cases
Edge Cases
Edge Computing
Edge Computing
Edge Detection
Edge Detection
Elastic Net
Elastic Net
Embedding Spaces
Embedding Spaces
Ensemble Learning
Ensemble Learning
Epoch
Epoch
Expectation-Maximization Algorithm (EM)
Expectation-Maximization Algorithm (EM)
Extreme Learning Machine
Extreme Learning Machine
F
F
F1 Score
F1 Score
FP-Growth Algorithm
FP-Growth Algorithm
Factor Analysis
Factor Analysis
False Positive Rate
False Positive Rate
Feature
Feature
Feature Engineering
Feature Engineering
Feature Extraction
Feature Extraction
Feature Hashing
Feature Hashing
Feature Learning
Feature Learning
Feature Scaling
Feature Scaling
Feature Selection
Feature Selection
Feature Vector
Feature Vector
Few-shot Learning
Few-shot Learning
Fisher’s Linear Discriminant
Fisher’s Linear Discriminant
Foundation Models
Foundation Models
Frame Rate
Frame Rate
Frames Per Second (FPS)
Frames Per Second (FPS)
Fully Connected Layer
Fully Connected Layer
Fuzzy Logic
Fuzzy Logic
G
G
Generative Adversarial Network (GAN)
Generative Adversarial Network (GAN)
Generative Adversarial Networks
Generative Adversarial Networks
Generative Pre-Trained Transformer
Generative Pre-Trained Transformer
C

Collaborative Filtering

Collaborative filtering is a technique used in recommender systems to predict a user’s preferences by leveraging the preference patterns of many users. The core idea is often summarized as “people who are similar to you liked X, so you might also like X” (user-based perspective) or “items that are similar to what you liked before were liked by you and others” (item-based perspective). Collaborative filtering operates on a user–item interaction matrix (e.g. users vs. movies with ratings): it doesn’t require any information about the items themselves (such as genre or description) – instead, it relies purely on the feedback (ratings, clicks, purchases) that users give to items. By “exploiting the wisdom of the crowd”​, the system can make surprisingly accurate recommendations: for instance, even if a new user has never watched a particular movie, if that user’s rating pattern is similar to a group of other users, and those users loved the movie, the system can recommend it.There are two primary approaches to collaborative filtering: user-based and item-based:User-based collaborative filtering: Find users who have historically exhibited similar taste to the target user, and recommend items that those similar users (often called the “neighbors”) liked​. For example, if Alice and Bob have rated many movies similarly, and Bob has highly rated a movie that Alice hasn’t seen, that movie would be recommended to Alice. The similarity between users can be computed by comparing their rating vectors (using cosine similarity, Pearson correlation, etc.). This approach essentially assumes that like-minded people will continue to agree on new items.Item-based collaborative filtering: Instead of comparing users, compare items based on the users who interact with them​.In this approach, the system looks at the target user’s liked items and finds other items that are similar to those liked items (where similarity between two items is determined by how the entire user base rated them). For instance, if many users who watched “The Lord of the Rings” also highly rated “Harry Potter”, then “Harry Potter” might be recommended to someone who enjoyed “The Lord of the Rings.” This method assumes that items can form clusters or associations (the “item affinity” perspective), and it often works well when there are many more users than items, because item-item similarities can be more stable and quicker to compute in large systems.Collaborative filtering can be implemented with memory-based methods (the above neighborhood approaches) or model-based methods. Model-based collaborative filtering typically uses matrix factorization or latent factor models (e.g., singular value decomposition or modern variations like implicit ALS) to decompose the user-item interaction matrix into latent features. These latent factors automatically capture user tastes and item characteristics (for example, in a movie context, one latent dimension might correspond to a preference for “action” vs “romance”). One challenge with collaborative filtering is the cold start problem: it requires sufficient user-item interactions to make reliable recommendations. New items (with no ratings) or new users (who haven’t rated anything) are hard to recommend with pure collaborative filtering. In practice, systems mitigate this by using content-based information or by prompting initial ratings. Despite such challenges, collaborative filtering remains a dominant approach in recommender systems (powering recommendations on e-commerce sites, streaming services, etc.) because it automatically personalizes to a user’s taste without needing explicit content analysis, simply by learning from the collective behavior of users​.

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