Support Vector Machines (SVMs) are supervised learning models used for classification and regression analysis. They map training examples to points in space to maximize the gap between two categories, and can also use the kernel trick to perform non-linear classification. Support vector clustering is an unsupervised learning approach that uses the statistics of support vectors to categorize unlabeled data.
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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