Types of Machine Learning

Supervised Learning

Overview

• Learning from labeled data
• Input-output pairs
• Prediction tasks
• Direct feedback

Key Characteristics

• Labeled training data
• Clear target variable
• Error measurement
• Model evaluation

Common Tasks

• Classification
• Regression
• Sequence prediction
• Ranking

Unsupervised Learning

Overview

• Learning from unlabeled data
• Pattern discovery
• Structure identification
• No explicit feedback

Key Characteristics

• No labeled data
• Pattern-based learning
• Internal evaluation
• Exploratory analysis

Common Tasks

• Clustering
• Dimensionality reduction
• Anomaly detection
• Association rules

Semi-Supervised Learning

Overview

• Combination of labeled and unlabeled data
• Partial supervision
• Hybrid approach
• Cost-effective learning

Key Characteristics

• Limited labeled data
• Large unlabeled dataset
• Combined learning strategies
• Balanced approach

Common Tasks

• Label propagation
• Self-training
• Co-training
• Active learning

Reinforcement Learning

Overview

• Learning through interaction
• Trial and error
• Reward-based feedback
• Sequential decision making

Key Characteristics

• Agent-environment interaction
• Delayed rewards
• Exploration vs exploitation
• Policy learning

Common Tasks

• Game playing
• Robot control
• Resource management
• Optimization

Transfer Learning

Overview

• Knowledge transfer between tasks
• Pre-trained models
• Domain adaptation
• Knowledge reuse

Key Characteristics

• Source and target domains
• Model adaptation
• Feature transfer
• Fine-tuning

Common Tasks

• Computer vision
• Natural language processing
• Speech recognition
• Cross-domain learning

Meta-Learning

Overview

• Learning to learn
• Algorithm selection
• Hyperparameter optimization
• Automated learning

Key Characteristics

• Meta-knowledge
• Learning strategies
• Adaptation mechanisms
• Efficiency optimization

Common Tasks

• Few-shot learning
• Architecture search
• Hyperparameter tuning
• Model selection