cognitive/docs/guides/machine_learning.md

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

class CognitiveModel:
    def __init__(self):
        self.belief_network = BeliefNetwork()
        self.action_policy = ActionPolicy()
        self.perception_model = PerceptionModel()

Learning Components

class LearningModule:
    def __init__(self):
        self.optimizer = FreeEnergyOptimizer()
        self.inference = VariationalInference()
        self.memory = EpisodicMemory()

Training Process

1. Data Preparation

   def prepare_data(self, observations):
       """Prepare data for learning."""
       return self.preprocessor.transform(observations)
   

2. Model Training

   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

   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

class ActiveInferenceLearner:
    """Integrate learning with active inference."""
    def __init__(self):
        self.model = ActiveInferenceModel()
        self.learner = LearningModule()

With Predictive Processing

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

# Initialize model
model = CognitiveModel()

# Train model
data = load_training_data()
model.train(data)

# Evaluate
results = model.evaluate(test_data)

Advanced Learning

# 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