Exploring Aa 19 20 Lecture 22
Exploring Aa 19 20 Lecture 22 reveals several interesting facts.
- SVM: soft margins, kernel trick, overfitting and regularization. Assignment 1.
- Classification. Linear separability and discriminants. Logistic Regression. Using linear classifiers in higher dimensions.
- Hierarchical Clustering. Agglomerative and Divisive Clustering.
- Deep learning. The problem of backpropagation. Autoencoders and Stacked Denoising Autoencoders.
- Introduction to clustering. K-means and k-medoids. Expectation maximization.
In-Depth Information on Aa 19 20 Lecture 22
Introduction to deep learning. Ensemble methods: bagging and boosting. Supervised learning, minimization (least squares), polynomial regression. Fuzzy sets and clustering. Fuzzy c-means. Manifold learning. Second assignment.
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