A) Explainable Artificial Intelligence (XAI)

• Interpretability and explainability
• Foundations and motivations of XAI
• Post-hoc explanation methods
• LIME (Local Interpretable Model-Agnostic Explanations)
• Feature attribution and saliency-based explanations
• Local and global explanations

B) Representation Learning

• Neural embeddings and latent spaces
• Principles of representation learning
• Skip-Gram and word embedding models
• Graph representation learning
• Introduction to Knowledge Graphs
• Knowledge graph embeddings

C) Attention and Knowledge-Based Reasoning

• Knowledge representation and reasoning
• Translational embedding models for knowledge graphs
• Attention mechanisms
• Query-Key-Value framework
• Attention in modern AI systems and Transformers

D) Transfer Learning and Continual Learning

• Foundations of transfer learning
• Inductive transfer learning
• Transductive transfer learning
• Unsupervised transfer learning
• Domain adaptation and domain alignment
• Representation transfer
• Practical applications of transfer learning
• Introduction to continual and lifelong learning
• Catastrophic forgetting and the stability-plasticity dilemma
• Regularization-based continual learning methods
• Knowledge distillation approaches for continual learning
• Evaluation of knowledge transfer, retention, and forgetting