AIDA Taxonomy

Our Taxonomy serves as an entry point to well-organized and easily accessible educational resources in the field of Artificial Intelligence (AI), continuously refined, to ensure it accurately captures its diverse, interdisciplinary, and evolving nature.
The core innovation of our initiative lies in the creation of a hierarchical taxonomy, a structured classification system that enables the categorization and tagging of educational materials.

This taxonomy is designed to reflect the commonalities and thematic elements present in AI-related content, allowing for precise indexing, retrieval, and curation of resources.
Its development involved a rigorous analysis of educational materials, expert input, and iterative refinement, allowing for the inclusion of emerging topics that are relevant to the curriculum, and ensuring that students are equipped with the most relevant knowledge and skills.

The taxonomy is divided in two main areas: AI Core Modules and Specialized Topic Pillars. The AI Core Modules incorporates 7 topics: Foundations of AI; AI Paradigms and Representations; Deciding and Learning How to Act; Machine Learning; Computer Vision; Natural Language Processing and Analysis; AI Ethics and Governance. As for the Specilized Topic Pillars, it includes 4 categories: AI for Media, Society and Democracy; Integrating Approaches for Trustworthy AI; Data-Driven Learning and Human-Centric AI.

Discover the full taxonomy below.

AI Core Modules

  1. Foundations of AI
    1. Definitions and History of AI
    2. Symbolic and Subsymbolic AI
    3. Data-driven AI
    4. Intelligent/rational agents
    5. Reasoning algorithms
  2. AI Paradigms and Representations
    1. Knowledge representation
    2. Description logics and ontologies
    3. Semantic networks
    4. Knowledge Graphs
    5. Search algorithms
    6. Optimization and metaheuristics
    7. Constraint satisfaction
  3. Deciding and Learning How to Act
    1. Logic and inference
    2. Model-based reasoning
    3. Expert systems
    4. Case-based reasoning
    5. Probabilistic description logics
    6. Default reasoning and belief revision
    7. Probabilistic/fuzzy logic and programming
    8. Decision theory and Markov Decision Processes
    9. Multi-agent Decision Making (ZAR)
    10. Logic and answer set programming
    11. Statistical relational learning
    12. Bayesian networks
    13. Bayesian filtering
    14. Automated planning
    15. Sequential decision making
  4. Machine Learning
    1. Basics
      1. Statistical foundations of machine learning
      2. Optimization for machine learning
      3. Model validation and selection
      4. Feature extraction and selection
      5. Overfitting and regularization
    2. Learning paradigms and settings
      1. Supervised learning
      2. Semi-supervised learning
      3. Unsupervised learning
      4. Cluster analysis
      5. Dimensionality reduction
      6. Active learning
      7. Cost-sensitive learning
      8. Manifold learning
      9. Anomaly detection
      10. Reinforcement learning
      11. Imitation and apprenticeship learning
      12. Multi-agent reinforcement learning
      13. Learning to rank
      14. Density estimation
      15. Topic modeling
      16. Adversarial learning
      17. Multi-task learning
      18. Transfer learning
      19. Domain adaptation
      20. Batch learning
      21. On-line learning
      22. Lifelong machine learning
      23. Federated learning
    3. Machine learning approaches
      1. Linear models for regression
      2. Linear models for classification
      3. Naive Bayes classifiers
      4. Non-parametric classifiers
      5. Clustering
      6. Gaussian Mixture Models
      7. Spectral methods
      8. Dimensionality reduction methods
      9. Neural networks
      10. Support Vector Machines
      11. Kernel machines
      12. Probabilistic graphical models
      13. Logical and relational learning
      14. Factorization methods
      15. Rule learning
      16. Representation learning
  5. Computer Vision
    1. Image acquisition and formation
    2. Image representation
    3. Shape representation
    4. 2D image registration
    5. Edge detection and feature extraction
    6. Feature matching
    7. Image segmentation and saliency detection
    8. Video capture and formats
    9. Video segmentation
    10. Camera calibration
    11. Projective and epipolar geometry
    12. Stereoscopic, 3D and multi-view imaging
    13. Depth and shape inference
    14. 3D reconstruction and Structure-from-Motion
    15. Visual Simultaneous Localization and Mapping
    16. Active vision
    17. Object recognition
    18. Biometric recognition
    19. Scene understanding
    20. Activity recognition and understanding
    21. Object detection
    22. Object tracking
    23. Semantic and instance segmentation
    24. Image/video captioning
    25. Video summarization
    26. Visual content-based indexing and retrieval
    27. Scene anomaly detection
    28. Computational photography
    29. Hyperspectral imaging
  6. Natural Language Processing and Analysis
    1. Information extraction
    2. Discourse, dialogue and pragmatics
    3. Lexical semantics
    4. Phonology / morphology
    5. Language resources
    6. Machine translation
    7. Text representation and word embeddings
    8. Natural language generation
    9. Large language models
    10. Text summarization
    11. Text classification
    12. Text mining
  7. AI Ethics and Governance
    1. Fundamentals of AI Ethics & Responsible AI
      1. AI Ethics overview
      2. What is Trustworthy AI 
      3. Trustworthy AI principles 
      4. Trustworthy AI principles in Europe 
      5. What is Responsible AI 
      6. The value alignment problem 
    2. AI Legal perspectives
      1. AI Legal perspectives across the world
      2. Relevant Legal Frameworks in Europe 
      3. Data Act 
      4. Digital Services Act 
      5. AI Liability Directive
      6. AI Act 
    3. Governing AI: current AI policy initiatives worldwide
      1. National Level – AI Governance
        • Global AI policy initiatives
        • EU AI policy initiatives 
        • The role of civil society 
      2. Corporate Level – AI Governance 
        • Global perspectives 
        • EU perspectives 
    4. Applying AI Ethics and Assessment frameworks
      • AI Ethics Assessment frameworks overview 
      • The Assessment List for Trustworthy AI (ALTAI Tool, HLEG) 
      • AI Governance 
      • Models for sustainable data governance

Specialized Topic Pillars

  1. AI for Media, Society and Democracy
    1. Music/Sound Analysis and Synthesis
      • Audio source separation
      • Acoustic scene classification
      • Sound event detection
      • Acoustic anomaly detection
      • Music tagging
      • Music transcription, indexing and retrieval
      • Music synthesis
      • Music similarity estimation
      • Music tempo estimation
      • Musical instrument recognition
      • Music genre recognition
      • Speech recognition
      • Speech synthesis
    2. AI and Game Media
      • Procedural game content generation
      • AI-assisted game design (AIAD)
      • Game analytics/data mining
      • Game player modeling
      • Gameplay enhancement
      • AI-enhanced game graphics
      • Game content personalization
      • Game testing
    3. Web and Social Media Analysis/Mining
      • Complex network analysis
      • Link analysis and prediction
      • Random graph models
      • Community detection
      • Node classification
      • Network information diffusion
      • Graph signal processing
      • Recommender systems
      • Ranking-based information retrieval
      • Semantic Web
      • Blockchain algorithms
      • Blockchain technology and applications
    4. Human-Centered Media Analysis
      • Emotion analysis
      • Facial feature detection
      • Face detection
      • Face and object de-detection
      • Face recognition
      • Face clustering
      • Face de-identification for privacy protection
      • Facial expression recognition
      • Speech segmentation
      • Speaker recognition
      • Visual speech recognition
      • Speech emotion recognition
      • Text affect detection
      • Physiological monitoring
      • Body/hand gesture recognition
      • Crowd detection and analysis
      • Human body posture and pose estimation
      • Human action recognition
      • Athlete motion analysis
  2. Integrating Approaches for Trustworthy AI
    1. Foundations of Trustworthy AI
      • AI explainability
      • AI safety
      • AI fairness
      • Accountability and reproducibility
      • Privacy
      • Sustainability
    2. Reasoning and Learning in Social Contexts
      • Social cognition modeling
      • Collaboration and teamwork modeling
      • Cooperation between agents
      • Learning from others
      • Emergent behaviour 
      • Agent societies and social networks
    3. Automated AI
      • Automated algorithm configuration
      • Automated algorithm selection
      • Automated performance prediction
      • Model selection
      • Hyperparameter optimization
      • Neural architecture search
  3. Data-Driven Learning
    1. Deep learning
      • Artificial Neural Networks, Perceptrons
      • Multilayer Perceptrons
      • Convolutional Neural Networks
      • Deep Autoencoders
      • Graph Neural Networks
      • Recurrent Neural Networks
      • Attention and Transformer networks
      • Neurosymbolic AI
    2. Machine Learning Theory
      • Statistics
      • Probability
      • Game theory
      • Numerical analysis
      • Analysis of algorithms
    3. Reinforcement learning and Sequential Decision-making
      • Online learning
        • Online learning algorithms
      • Bandits
        • Action-Value methods
        • Gradient Bandit methods
      • Model-based RL
        • Given the model
        • Learning the model
      • Model-free RL
        • Q-learning
        • Policy optimization
      • Deep Reinforcement Learning
    4. Distributed and Federated learning
      • Multi-Agent Systems
        • Agent-oriented software engineering
        • Distributed problem solving and planning
        • Search algorithms for agents
        • Cooperation and coordination of agents
        • Multi-agent learning
        • Agent negotiation
        • Beliefs, desires, and intention (BDI)
        • Agent negotiation and cooperation
        • Distributed constraint optimization (DCOPs)
        • Applications of multi-agent systems
      • Distributed and Federated learning
        • Distributed ensemble learning
        • Mobile Edge AI
        • Distributed deep learning
        • Resource management and scheduling in federated learning
        • Communication network optimization in federated learning
        • Energy Efficiency in federated learning
        • Ultra-light DNN architectures in federated learning
        • Applications of federated learning
        • Privacy in federated learning
        • Reliability in federated learning
        • Resource heterogeneity in federated learning
    5. Generative Artificial Intelligence
      • Variational Autoencoders
      • Generative Adversarial Networks
      • Diffusion Models
      • Image/video generation
      • Text/code generation
      • Prompting
      • Generative AI applications
  4. Human-Centric AI
    1. Human-centered Machine Learning
      • Interactive machine learning/Learning from human feedback
      • Understand human teaching strategies
      • Argumentation
      • Social simulation
      • Multimodal Perception and Modeling
        • Common Ground
        • Embodiment
        • Multimodal communication
        • Cognitive Architecture
      • Human-AI Interaction and Collaboration
        • Shared mental models
        • Theory of Mind
        • Human-AI Communication
        • Human-Robot Interaction
        • Verbal Communication
        • Non-verbal Communication
        • Common Ground
        • Cooperation
        • Dialog Systems
        • Pragmatics
        • Human-Aware Planning
        • Chatbots
        • Social robots
      • Social Awareness 
        • Critical Study
        • Participatory Design
        • Social Cues
        • Hybrid Human-AI
        • Artificial Social Intelligence
        • Norms 
    2. Explainable AI
      • Interpretability and transparency
      • Explanations in social sciences literature
      • Local and global explanation methods
      • Interpretable models
      • Post-hoc explanations
      • Explainability in embodied and non-embodied AI systems
      • Causality and interactivity
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