Semi-supervised learning is a branch of machine learning that combines a small amount of labeled data with a large amount of unlabeled data during training. It is motivated by the cost associated with obtaining labeled data and can produce considerable improvement in learning accuracy. It may refer to either transductive or inductive learning, depending on the goal of inferring labels for given unlabeled data or inferring a mapping from input space to output space.
Carnegie Mellon University
Spring 2018
A comprehensive exploration of machine learning theories and practical algorithms. Covers a broad spectrum of topics like decision tree learning, neural networks, statistical learning, and reinforcement learning. Encourages hands-on learning via programming assignments.
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