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 the three main categories of machine learning, each defined by how they learn from data. In supervised learning, models are trained on labeled datasets where the input and the correct output are already known, allowing the system to learn patterns and make predictions (e.g., classification and regression tasks). Unsupervised learning, on the other hand, deals with unlabeled data, where the model identifies hidden structures or patterns such as clusters or associations without predefined outcomes. Reinforcement learning is based on an agent interacting with an environment, learning to make decisions by receiving rewards or penalties for its actions, thereby improving its strategy over time; this approach is commonly used in robotics, gaming, and autonomous systems.
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.
