# Machine Learning Guide
## Overview
This guide covers machine learning concepts, implementations, and best practices in the cognitive modeling framework.
## Core Concepts
### Learning Paradigms
1. Active Inference
- Free energy minimization
- Belief updating
- Action selection
- See [[knowledge_base/cognitive/active_inference]]
1. Predictive Processing
- Prediction error minimization
- Hierarchical inference
- Precision weighting
- See [[knowledge_base/cognitive/predictive_processing]]
1. Variational Methods
- Variational inference
- Variational Bayes
- Mean field approximation
- See [[knowledge_base/mathematics/variational_inference]]
## Implementation
### Model Architecture
#### Base Components
```python
class CognitiveModel:
def __init__(self):
self.belief_network = BeliefNetwork()
self.action_policy = ActionPolicy()
self.perception_model = PerceptionModel()
```
#### Learning Components
```python
class LearningModule:
def __init__(self):
self.optimizer = FreeEnergyOptimizer()
self.inference = VariationalInference()
self.memory = EpisodicMemory()
```
### Training Process
1. Data Preparation
```python
def prepare_data(self, observations):
"""Prepare data for learning."""
return self.preprocessor.transform(observations)
```
1. Model Training
```python
def train_model(self, data, epochs=100):
"""Train the cognitive model."""
for epoch in range(epochs):
self.update_beliefs(data)
self.optimize_policy()
self.evaluate_performance()
```
1. Evaluation
```python
def evaluate_model(self, test_data):
"""Evaluate model performance."""
predictions = self.model.predict(test_data)
metrics = self.compute_metrics(predictions)
return metrics
```
## Best Practices
### Model Design
1. Use hierarchical architectures
1. Implement belief updating
1. Enable active inference
1. Support online learning
### Training Process
1. Validate assumptions
1. Monitor convergence
1. Track metrics
1. Save checkpoints
### Evaluation Methods
1. Use multiple metrics
1. Test edge cases
1. Validate stability
1. Compare baselines
## Advanced Topics
### Transfer Learning
- Pre-trained models
- Domain adaptation
- Knowledge transfer
- See [[knowledge_base/cognitive/transfer_learning]]
### Meta-Learning
- Learning to learn
- Adaptation strategies
- Parameter optimization
- See [[knowledge_base/cognitive/meta_learning]]
### Continual Learning
- Catastrophic forgetting
- Memory consolidation
- Stability-plasticity
- See [[knowledge_base/cognitive/continual_learning]]
## Integration
### With Active Inference
```python
class ActiveInferenceLearner:
"""Integrate learning with active inference."""
def __init__(self):
self.model = ActiveInferenceModel()
self.learner = LearningModule()
```
### With Predictive Processing
```python
class PredictiveLearner:
"""Integrate learning with predictive processing."""
def __init__(self):
self.model = PredictiveModel()
self.learner = LearningModule()
```
## Validation
### Model Validation
1. Test prediction accuracy
1. Validate belief updates
1. Check action selection
1. Verify learning stability
### Performance Metrics
1. Prediction error
1. Free energy
1. Action efficiency
1. Learning rate
## Examples
### Basic Learning
```python
# Initialize model
model = CognitiveModel()
# Train model
data = load_training_data()
model.train(data)
# Evaluate
results = model.evaluate(test_data)
```
### Advanced Learning
```python
# Initialize with transfer learning
model = CognitiveModel(pretrained=True)
# Configure meta-learning
model.enable_meta_learning()
# Train with continual learning
model.train_continual(data_stream)
```
## Troubleshooting
### Common Issues
1. Convergence problems
1. Memory limitations
1. Performance bottlenecks
1. Integration errors
### Solutions
1. Adjust learning rates
1. Optimize memory usage
1. Profile performance
1. Debug integration
## Related Documentation
- [[knowledge_base/cognitive/active_inference]]
- [[knowledge_base/cognitive/predictive_processing]]
- [[knowledge_base/mathematics/variational_inference]]
- [[docs/guides/ai_validation_framework]]