Exploring Aa 19 20 Lecture 2
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- Classification. Linear separability and discriminants. Logistic Regression. Using linear classifiers in higher dimensions.
- Introduction.
- Fuzzy sets and clustering. Fuzzy c-means. Manifold learning. Second assignment.
- Empirical Risk Minimization. Decision theory. Probably Approximately Correct Learning. VC dimension and shattering.
- Hierarchical Clustering. Agglomerative and Divisive Clustering.
In-Depth Information on Aa 19 20 Lecture 2
Supervised learning, minimization (least squares), polynomial regression. Introduction to deep learning. Andrew Johnson, the Radicals, and the Second American Revolution. In this DeVane Ensemble methods: bagging and boosting.
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