The VC dimension is a measure of the complexity or flexibility of a set of functions that can be learned by a binary classification algorithm. It is defined as the largest set of points that the algorithm can perfectly classify, regardless of their labeling. The capacity of a classification model is related to how complicated it can be, with higher capacity models being more flexible but potentially prone to errors.
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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