Nigeria's free digital learning hub. Videos, lecture notes, practice questions and leaderboards aligned with CCMAS, NBTE, NCCE, WAEC, NECO, IJMB and JAMB curricula.
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Nigeria's free digital learning hub. Videos, lecture notes, practice questions and leaderboards aligned with CCMAS, NBTE, NCCE, WAEC, NECO, IJMB and JAMB curricula.
Supervised, unsupervised, and reinforcement learning are three core types of machine learning approaches that differ in how they learn from data. Supervised learning uses labeled datasets, where the correct output is already known, enabling the model to learn a mapping from inputs to outputs (e.g., classification or regression tasks). In contrast, unsupervised learning works with unlabeled data and aims to discover hidden patterns or structures within the data, such as clustering similar items or reducing dimensionality. Meanwhile, reinforcement learning involves an agent that learns by interacting with an environment, receiving rewards or penalties based on its actions, and gradually improving its decision-making strategy to maximize long-term rewards. Together, these approaches form the foundation of modern artificial intelligence systems, each suited to different types of problems and data availability.
This lecture is part of CSC316 MACHINE LEARNING in the Computer Science department. The course has 30 recorded lectures across 10 series, all free to watch.
