Probabilistic learning is a research field and methodological approach within machine learning and artificial intelligence focused on developing systems that learn from data by explicitly modeling and quantifying uncertainty. It investigates methods for inferring probability distributions over model parameters, predictions, and latent variables, utilizing principles from probability theory and statistics to build robust models capable of making decisions and quantifying confidence in their outputs in stochastic environments. Its significance lies in enabling the development of intelligent systems that can handle noisy or incomplete data and provide principled estimates of prediction reliability.