---
title: Machine Learning Guide
type: guide
status: draft
created: 2024-02-12
tags:
  - machine-learning
  - ai
  - guide
semantic_relations:
  - type: implements
    links: [[ai_validation_framework]]
  - type: relates
    links:
      - [[active_inference]]
      - [[predictive_processing]]
---

# 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]]

2. Predictive Processing
   - Prediction error minimization
   - Hierarchical inference
   - Precision weighting
   - See [[knowledge_base/cognitive/predictive_processing]]

3. 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)
   ```

2. 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()
   ```

3. 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
2. Implement belief updating
3. Enable active inference
4. Support online learning

### Training Process
1. Validate assumptions
2. Monitor convergence
3. Track metrics
4. Save checkpoints

### Evaluation Methods
1. Use multiple metrics
2. Test edge cases
3. Validate stability
4. 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
2. Validate belief updates
3. Check action selection
4. Verify learning stability

### Performance Metrics
1. Prediction error
2. Free energy
3. Action efficiency
4. 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
2. Memory limitations
3. Performance bottlenecks
4. Integration errors

### Solutions
1. Adjust learning rates
2. Optimize memory usage
3. Profile performance
4. Debug integration

## Related Documentation
- [[knowledge_base/cognitive/active_inference]]
- [[knowledge_base/cognitive/predictive_processing]]
- [[knowledge_base/mathematics/variational_inference]]
- [[docs/guides/ai_validation_framework]]