A-Z of Machine Learning and Computer Vision Terms
A
AI Agent
AI Assistants
AI Assisted Labeling
Active Learning
Algorithm
Anchor Boxes
Anomaly Detection
Artificial Intelligence (AI)
Attribute
B
Backpropagation
Bagging
Batch
Batch Normalization
Bayesian Network
Bias
Big Data
Binary Classification
Blur
Boosting
Bounding Box
C
COCO
Calibration
Calibration Curve
Canonical Correlation Analysis (CCA)
Case-Based Reasoning
Chain of Thought (CoT)
ChatGPT
Chi-Squared Automatic Interaction Detection (CHAID)
Class Boundary
Class Boundary (Statistics & Machine Learning)
Class Imbalance
Clustering
Collaborative Filtering
Computer Vision
Computer Vision Model
Concept Drift
Conditional Random Field (CRF)
Confusion Matrix
Constrained Clustering
Contrastive Learning
Convolutional Neural Network (CNN)
Convolutional Neural Networks (CNNs)
Cross-Validation
D
DICOM
Data Approximation
Data Augmentation
Data Drift
Data Error
Data Mining
Data Operations
Data Pre-processing
Data Quality
Dataset
Decision Boundary
Decision List
Decision Stump
Decision Tree
Deep Learning
Deep Neural Networks
Dimensionality Reduction
Dropout
Dynamic and Event-Based Classifications
E
Edge Cases
Edge Computing
Edge Detection
Elastic Net
Embedding Spaces
Ensemble Learning
Epoch
Expectation-Maximization Algorithm (EM)
Extreme Learning Machine
F
F1 Score
FP-Growth Algorithm
Factor Analysis
False Positive Rate
Feature
Feature Engineering
Feature Extraction
Feature Hashing
Feature Learning
Feature Scaling
Feature Selection
Feature Vector
Few-shot Learning
Fisher’s Linear Discriminant
Foundation Models
Frame Rate
Frames Per Second (FPS)
Fully Connected Layer
Fuzzy Logic
G
Generative Adversarial Network (GAN)
Generative Adversarial Networks
Generative Pre-Trained Transformer
Reinforcement Learning
Reinforcement learning is a paradigm of machine learning where an agent learns to make sequences of decisions by interacting with an environment and receiving feedback in the form of rewards or punishments.
The learning is trial-and-error based: the agent takes an action in a given state, the environment responds with a new state and a reward signal, and the agent updates its policy (behavior strategy) accordingly. Over time, the agent’s objective is to learn a policy that maximizes cumulative reward. Key concepts in RL include states (the situation the agent is in), actions (choices the agent can make), rewards (numeric feedback), and the policy (mapping from states to actions).
RL algorithms can be model-free (learning directly from experience, e.g., Q-learning, policy gradients) or model-based (using a model of the environment’s dynamics). This approach has achieved notable success in areas like game playing (AlphaGo), robotics, and resource management.
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