Kernel machines are a class of algorithms for pattern analysis that use linear classifiers to solve nonlinear problems. They rely on kernel functions and convex optimization or eigenproblems, and are typically analyzed using statistical learning theory. Kernel methods are computationally expensive for datasets larger than a few thousand examples.
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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