Algorithmic learning theory is a subfield of machine learning and computational learning theory that focuses on the study of algorithms that can learn from data and improve their performance over time. It combines concepts from algorithm design, statistical learning, and information theory to understand and formalize how machines can uncover patterns, make predictions, and make decisions based on data.
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