Episode Synopsis "Empirical Risk Minimization"

The concept of empirical risk minimization drives modern approaches to training many machine learning algorithms, including deep neural networks. Today's thirty second summary covers the basics of what you need to know, but the concept goes well beyond just the simple case we discuss today. If you are looking to discuss the topic further, please consider joining the conversation on Twitter. Lecture notes from Carnegie Mellon University (no affiliation).

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More episodes of the podcast Machine Learning Bytes

• K-Fold Cross Validation
• Stratified Sampling
• Boosting
• Bagging
• Bias, Variance, and the Bias-Variance Tradeoff
• Empirical Risk Minimization