Exploring Aa 17 18 Lecture 10
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- Introduction to clustering. K-means and k-medoids. Expectation maximization.
- Deep learning. The problem of backpropagation. Autoencoders and Stacked Denoising Autoencoders.
- Generative models: naive bayes, bayes. Comparing classifiers. Assignment 1.
- Bayesian Decision theory. Maximum a posteriori estimation. Decisions and costs.
- Multiclass classification. Bootstrapping. Bias-variance decomposition and tradeoff.
In-Depth Information on Aa 17 18 Lecture 10
SVM: soft margins, kernel trick, overfitting and regularization. Graphical methods, Hidden markov models. The Baum-Welch and Vitterbi algorithms. Maximum Margin Classifiers. Support vector machines for linear classification. Ensemble methods: bagging and boosting.
Fuzzy sets and clustering. Fuzzy c-means. Probabilistic Clustering: mixture models. Expectation-Maximization revisited. Second ...
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