A-Z of Machine Learning and Computer Vision Terms

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Quantum Machine Learning
Quantum Machine Learning
Query Strategy (Active Learning)
Query Strategy (Active Learning)
Query Synthesis Methods
Query Synthesis Methods
R
R
RAG Architecture
RAG Architecture
ROC (Receiver Operating Characteristic) Curve
ROC (Receiver Operating Characteristic) Curve
Random Forest
Random Forest
Recall (Sensitivity or True Positive Rate)
Recall (Sensitivity or True Positive Rate)
Recurrent Neural Network (RNN)
Recurrent Neural Network (RNN)
Region Proposal Network (RPN)
Region Proposal Network (RPN)
Region-Based CNN (R-CNN)
Region-Based CNN (R-CNN)
Regression (Regression Analysis)
Regression (Regression Analysis)
Regularization Algorithms
Regularization Algorithms
Reinforcement Learning
Reinforcement Learning
Responsible AI
Responsible AI
S
S
Scale Imbalance
Scale Imbalance
Scikit-Learn
Scikit-Learn
Segment Anything Model (SAM)
Segment Anything Model (SAM)
Selective Sampling
Selective Sampling
Self-Supervised Learning
Self-Supervised Learning
Semantic Segmentation
Semantic Segmentation
Semi-supervised Learning
Semi-supervised Learning
Sensitivity and Specificity of Machine Learning
Sensitivity and Specificity of Machine Learning
Sentiment Analysis
Sentiment Analysis
Sliding Window Attention
Sliding Window Attention
Stream-Based Selective Sampling
Stream-Based Selective Sampling
Supervised Learning
Supervised Learning
Support Vector Machine (SVM)
Support Vector Machine (SVM)
Surrogate Model
Surrogate Model
Synthetic Data
Synthetic Data
T
T
Tabular Data
Tabular Data
Text Generation Inference
Text Generation Inference
Training Data
Training Data
Transfer Learning
Transfer Learning
Transformers (Transformer Networks)
Transformers (Transformer Networks)
Triplet Loss
Triplet Loss
True Positive Rate (TPR)
True Positive Rate (TPR)
Type I Error (False Positive)
Type I Error (False Positive)
Type II Error (False Negative)
Type II Error (False Negative)
U
U
Unsupervised Learning
Unsupervised Learning
V
V
Variance (Model Variance)
Variance (Model Variance)
Variational Autoencoders
Variational Autoencoders
W
W
Weak Supervision
Weak Supervision
Weight Decay (L2 Regularization)
Weight Decay (L2 Regularization)
X
X
XAI (Explainable AI)
XAI (Explainable AI)
XGBoost
XGBoost
Y
Y
YOLO (You Only Look Once)
YOLO (You Only Look Once)
Yolo Object Detection
Yolo Object Detection
Z
Z
Zero-Shot Learning
Zero-Shot Learning
H

Human in the Loop (HITL)

Human in the Loop refers to systems or processes where human intervention is actively used in combination with automated algorithms to improve outcomes. In machine learning, HITL is common in scenarios like active learning (where a model queries a human for labels on uncertain examples), interactive machine learning, or in monitoring model outputs to catch and correct errors (e.g., a human moderates outputs of an AI system to ensure quality). HITL acknowledges that fully autonomous AI isn’t always reliable or desirable, and human judgment can guide, correct, or augment automated processes. For example, in model training, a human might label data or adjust features; in deployment, a human might receive AI suggestions but make the final decision (common in medical diagnosis or loan approvals). HITL can significantly enhance system performance, especially in complex tasks that AI alone can’t solve perfectly, by leveraging human expertise and oversight.

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Region Proposal Network (RPN)
Class Boundary (Statistics & Machine Learning)
Long Short-Term Memory (LSTM)
Recurrent Neural Network (RNN)
Large Language Model (LLM)
Chain of Thought (CoT)
Foundation Models
Semantic Segmentation
Variance (Model Variance)
XAI (Explainable AI)
YOLO (You Only Look Once)
Weight Decay (L2 Regularization)
Text Generation Inference
True Positive Rate (TPR)
Type II Error (False Negative)
Type I Error (False Positive)
Triplet Loss
Transformers (Transformer Networks)
Tabular Data
Stream-Based Selective Sampling
Synthetic Data
Training Data
Support Vector Machine (SVM)
Sentiment Analysis
Surrogate Model
Supervised Learning
Semi-supervised Learning
Selective Sampling
Sliding Window Attention
Sensitivity and Specificity of Machine Learning
Scikit-Learn
Segment Anything Model (SAM)
Regularization Algorithms
ROC (Receiver Operating Characteristic) Curve
Scale Imbalance
Responsible AI
Regression (Regression Analysis)
Region-Based CNN (R-CNN)
Recall (Sensitivity or True Positive Rate)
Random Forest
RAG Architecture
Query Synthesis Methods
Query Strategy (Active Learning)
Predictive Model Validation
Prompt Injection
Prompt Engineering
PyTorch
Prompt Chaining
PR AUC
Precision
Pooling
Pose Estimation
Pool-Based Sampling
Pattern Recognition
Perceptron
Pixel
Parameter-Efficient Fine-Tuning (Prefix-Tuning)
Pandas and NumPy
Panoptic Segmentation
PACS (Picture Archiving and Communication System)
Overfitting
Outlier Detection
Object Tracking
Optical Character Recognition (OCR)
One-Shot Learning
Object Localization
Object Detection
Normalization
Noise
NIfTI
Natural Language Processing (NLP)
Neural Networks
Multi-Task Learning
Motion Detection
Motion Estimation
Model Validation
Latent Dirichlet Allocation (LDA)
Medical Image Segmentation
Model Parameters
Model Accuracy
Micro-Models
Mean Squared Error (MSE)
Keypoints
Mean Average Precision (mAP)
Machine Learning (ML)
Loss Function
Linear Regression
Linear Discriminant Analysis (LDA)
LIDAR
Learning Rate
Label Errors
Label
Intersection over Union (IoU)
Interpolation
Interpretability
Imbalanced Dataset
Image Processing
Image Restoration
Image Segmentation
Image Recognition

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