Machine Learning
A subset of AI where algorithms learn patterns from data to make predictions or decisions without explicit programming.
Detailed Definition
Machine Learning (ML) is a branch of artificial intelligence where computer algorithms learn patterns from data and improve their performance without being explicitly programmed for each task.
Types of machine learning
Supervised Learning: - Trained on labeled examples - Predicts outcomes for new data - Examples: grade prediction, classification
Unsupervised Learning: - Finds patterns in unlabeled data - Discovers natural groupings - Examples: geological clustering, anomaly detection
Reinforcement Learning: - Learns through trial and error - Optimizes decisions over time - Examples: mine planning, route optimization
GIS and mining applications
Geological modeling: - Ore body delineation - Grade estimation - Structural interpretation
Remote sensing: - Land cover classification - Change detection - Mineral mapping
Document processing: - Extracting data from plats and deeds - Classifying document types - OCR enhancement
Predictive analytics: - Equipment failure prediction - Production forecasting - Cost estimation
Machine learning enables processing of large datasets that would be impractical to analyze manually.
Related Terms
Artificial Intelligence
Computer systems designed to perform tasks that typically require human intelligence, such as pattern recognition and decision-making.
Deep Learning
A type of machine learning using neural networks with multiple layers to learn complex patterns from large datasets.
Geospatial AI
The application of artificial intelligence and machine learning techniques specifically to geographic and spatial data analysis.