cognitive/docs/guides/learning_paths/active_inference_learning_path.md

title	type	status	created	tags	semantic_relations
Active Inference Learning Path	learning_path	stable	2024-02-12	learning-path active-inference guide	type links implements ../documentation_standards type links relates ../../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 distributions
   • Bayesian inference
   • Information theory

2. knowledge_base/mathematics/variational_inference

   • Variational Bayes
   • Mean field approximation
   • Free energy principle

3. knowledge_base/mathematics/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

   • Biological foundations
   • Information theory perspective
   • Variational principles

2. knowledge_base/cognitive/predictive_processing

   • Hierarchical prediction
   • Error minimization
   • Precision weighting

Week 2: Active Inference

1. knowledge_base/cognitive/active_inference

   • Core principles
   • Mathematical framework
   • Implementation strategies

2. knowledge_base/cognitive/belief_updating

   • Message passing
   • Belief propagation
   • State estimation

2. Implementation Basics

Week 3: Core Components

1. knowledge_base/cognitive/generative_models

   • Model architecture
   • State space design
   • Observation models

2. knowledge_base/cognitive/inference_algorithms

   • Variational inference
   • Message passing
   • Policy selection

Week 4: Basic Implementation

1. docs/guides/implementation/basic_agent

   • Agent architecture
   • Belief updating
   • Action selection

2. docs/guides/implementation/simple_environment

   • Environment design
   • Interaction loop
   • Observation generation

3. Advanced Topics

Week 5: Advanced Features

1. knowledge_base/cognitive/hierarchical_models

   • Deep active inference
   • Temporal depth
   • Abstract reasoning

2. knowledge_base/cognitive/learning_mechanisms

   • Parameter learning
   • Structure learning
   • Meta-learning

Week 6: Applications

1. docs/guides/implementation/complex_environments

   • Partial observability
   • Continuous actions
   • Multi-agent systems

2. docs/guides/implementation/real_world_applications

   • Robotics
   • Decision support
   • Cognitive modeling

4. Research and Development

Week 7: Research Methods

1. docs/guides/research/experimental_design

   • Hypothesis testing
   • Ablation studies
   • Comparative analysis

2. docs/guides/research/evaluation_metrics

   • Performance metrics
   • Behavioral analysis
   • Model comparison

Week 8: Advanced Development

1. docs/guides/implementation/scaling_solutions

   • Distributed computing
   • Optimization techniques
   • Memory management

2. docs/guides/implementation/deployment

   • Production systems
   • Monitoring
   • Maintenance

Projects

Beginner Projects

1. docs/examples/mnist_classification

   • Basic perception
   • Simple actions
   • Performance evaluation

2. docs/examples/grid_world

   • Spatial reasoning
   • Path planning
   • Goal-directed behavior

Intermediate Projects

1. docs/examples/continuous_control

   • Motor control
   • Continuous actions
   • Dynamic environments

2. docs/examples/multi_agent

   • Agent interaction
   • Collective behavior
   • Emergent patterns

Advanced Projects

1. docs/examples/hierarchical_reasoning

   • Abstract planning
   • Meta-learning
   • Transfer learning

2. docs/examples/real_world_robotics

   • 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

   • Latest developments
   • Research frontiers
   • Open problems

2. docs/guides/learning_paths/research_track

   • Publication preparation
   • Conference participation
   • Collaboration opportunities

Related Paths

1. docs/guides/learning_paths/predictive_processing
2. docs/guides/learning_paths/cognitive_architectures
3. docs/guides/learning_paths/machine_learning

Related Documentation

• docs/guides/machine_learning
• docs/guides/research
• docs/guides/implementation/active_inference_implementation