Machine learning algorithms can be divided into 3 broad categories — supervised learning, unsupervised learning, and reinforcement learning.Supervised learning is useful in cases where a property (label) is available for a certain dataset (training set), but is missing and needs to be predicted for other instances. Unsupervised learning is useful in cases where the challenge is to discover implicit relationships in a given unlabeled dataset (items are not pre-assigned). Reinforcement learning falls between these 2 extremes — there is some form of feedback available for each predictive step or action, but no precise label or error message. Since this is an intro class, they didn’t learn about reinforcement learning, but they hope that 10 algorithms on supervised and unsupervised learning will be enough to keep you interested. This will be added to Artificial Intelligence Resources Subject Tracer™.
posted by Marcus Zillman |
4:49 AM
