---
title: Active Inference Learning Path
type: learning_path
status: stable
created: 2024-02-12
tags:
  - learning-path
  - active-inference
  - guide
semantic_relations:
  - type: implements
    links: [[../documentation_standards]]
  - type: relates
    links:
      - [[../../knowledge_base/cognitive/active_inference]]
      - [[../machine_learning]]
---

# Active Inference Learning Path

## Overview

This learning path provides a structured approach to understanding and implementing active inference in the cognitive modeling framework.

## Prerequisites

### Mathematics
1. [[knowledge_base/mathematics/probability_theory|Probability Theory]]
   - Probability distributions
   - Bayesian inference
   - Information theory

2. [[knowledge_base/mathematics/variational_inference|Variational Inference]]
   - Variational Bayes
   - Mean field approximation
   - Free energy principle

3. [[knowledge_base/mathematics/optimization_theory|Optimization Theory]]
   - Gradient descent
   - Expectation maximization
   - Variational methods

### Programming
1. Python Fundamentals
   - Object-oriented programming
   - Scientific computing (NumPy, SciPy)
   - Machine learning frameworks

2. Software Engineering
   - Version control
   - Testing
   - Documentation

## Learning Path

### 1. Theoretical Foundations

#### Week 1: Basic Concepts
1. [[knowledge_base/cognitive/free_energy_principle|Free Energy Principle]]
   - Biological foundations
   - Information theory perspective
   - Variational principles

2. [[knowledge_base/cognitive/predictive_processing|Predictive Processing]]
   - Hierarchical prediction
   - Error minimization
   - Precision weighting

#### Week 2: Active Inference
1. [[knowledge_base/cognitive/active_inference|Active Inference Theory]]
   - Core principles
   - Mathematical framework
   - Implementation strategies

2. [[knowledge_base/cognitive/belief_updating|Belief Updating]]
   - Message passing
   - Belief propagation
   - State estimation

### 2. Implementation Basics

#### Week 3: Core Components
1. [[knowledge_base/cognitive/generative_models|Generative Models]]
   - Model architecture
   - State space design
   - Observation models

2. [[knowledge_base/cognitive/inference_algorithms|Inference Algorithms]]
   - Variational inference
   - Message passing
   - Policy selection

#### Week 4: Basic Implementation
1. [[docs/guides/implementation/basic_agent|Basic Agent Implementation]]
   - Agent architecture
   - Belief updating
   - Action selection

2. [[docs/guides/implementation/simple_environment|Simple Environment]]
   - Environment design
   - Interaction loop
   - Observation generation

### 3. Advanced Topics

#### Week 5: Advanced Features
1. [[knowledge_base/cognitive/hierarchical_models|Hierarchical Models]]
   - Deep active inference
   - Temporal depth
   - Abstract reasoning

2. [[knowledge_base/cognitive/learning_mechanisms|Learning Mechanisms]]
   - Parameter learning
   - Structure learning
   - Meta-learning

#### Week 6: Applications
1. [[docs/guides/implementation/complex_environments|Complex Environments]]
   - Partial observability
   - Continuous actions
   - Multi-agent systems

2. [[docs/guides/implementation/real_world_applications|Real-world Applications]]
   - Robotics
   - Decision support
   - Cognitive modeling

### 4. Research and Development

#### Week 7: Research Methods
1. [[docs/guides/research/experimental_design|Experimental Design]]
   - Hypothesis testing
   - Ablation studies
   - Comparative analysis

2. [[docs/guides/research/evaluation_metrics|Evaluation Methods]]
   - Performance metrics
   - Behavioral analysis
   - Model comparison

#### Week 8: Advanced Development
1. [[docs/guides/implementation/scaling_solutions|Scaling Solutions]]
   - Distributed computing
   - Optimization techniques
   - Memory management

2. [[docs/guides/implementation/deployment|Deployment]]
   - Production systems
   - Monitoring
   - Maintenance

## Projects

### Beginner Projects
1. [[docs/examples/mnist_classification|MNIST Classification]]
   - Basic perception
   - Simple actions
   - Performance evaluation

2. [[docs/examples/grid_world|Grid World Navigation]]
   - Spatial reasoning
   - Path planning
   - Goal-directed behavior

### Intermediate Projects
1. [[docs/examples/continuous_control|Continuous Control]]
   - Motor control
   - Continuous actions
   - Dynamic environments

2. [[docs/examples/multi_agent|Multi-agent Coordination]]
   - Agent interaction
   - Collective behavior
   - Emergent patterns

### Advanced Projects
1. [[docs/examples/hierarchical_reasoning|Hierarchical Reasoning]]
   - Abstract planning
   - Meta-learning
   - Transfer learning

2. [[docs/examples/real_world_robotics|Robotics Integration]]
   - Physical systems
   - Real-time control
   - Safety constraints

## Resources

### Reading Materials
1. Core Papers
   - Original active inference papers
   - Key implementation papers
   - Recent developments

2. Books
   - Theoretical foundations
   - Implementation guides
   - Case studies

### Tools and Libraries
1. Framework Components
   - Core libraries
   - Extensions
   - Utilities

2. Development Tools
   - Debugging tools
   - Profiling tools
   - Visualization tools

## Assessment

### Knowledge Checks
1. Theoretical Understanding
   - Concept quizzes
   - Mathematical exercises
   - Paper reviews

2. Practical Skills
   - Coding exercises
   - Project implementation
   - Performance optimization

### Final Projects
1. Research Project
   - Novel implementation
   - Experimental validation
   - Documentation

2. Application Project
   - Real-world application
   - Performance analysis
   - Deployment strategy

## Next Steps

### Advanced Learning
1. [[docs/guides/learning_paths/advanced_active_inference|Advanced Active Inference]]
   - Latest developments
   - Research frontiers
   - Open problems

2. [[docs/guides/learning_paths/research_track|Research Track]]
   - Publication preparation
   - Conference participation
   - Collaboration opportunities

### Related Paths
1. [[docs/guides/learning_paths/predictive_processing|Predictive Processing]]
2. [[docs/guides/learning_paths/cognitive_architectures|Cognitive Architectures]]
3. [[docs/guides/learning_paths/machine_learning|Machine Learning]]

## Related Documentation
- [[docs/guides/machine_learning]]
- [[docs/guides/research]]
- [[docs/guides/implementation/active_inference_implementation]]