Structural risk minimization (SRM) is a principle in machine learning that addresses the problem of overfitting by balancing a model's complexity with its success at fitting the training data. It involves minimizing the sum of the training error and a regularization function, which controls the trade-off between minimizing the training error and minimizing the expected gap between the training error and test error. This principle can be implemented by placing a prior over the weights of the model to favor sparsity and penalize larger weights.
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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