Understanding Aa 18 19 Lecture 13
If you are looking for information about Aa 18 19 Lecture 13, you have come to the right place. Empirical Risk Minimization. Decision theory. Probably Approximately Correct Learning. VC dimension and shattering. Bayesian ...
Key Takeaways about Aa 18 19 Lecture 13
- Dimensionality reduction: feature extraction with PCA; self-organzing maps.
- Overfitting and regularization with polynomial regression. Select models: Train, validate, test.
- Affinity Propagation clustering and problems with prototype-based clustering. Density Clustering.
- Empirical Risk Minimization. Decision theory. Probably Approximately Correct Learning. VC dimension and shattering.
- Ensemble methods: bagging and boosting.
Detailed Analysis of Aa 18 19 Lecture 13
Decisions and costs. Hierarchical Clustering. Agglomerative and Divisive Clustering. Clustering Features. Introduction.
Classification. Linear separability and discriminants. Logistic Regression. Using linear classifiers in higher dimensions.
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