Prerequisites: MATH 3339
- ”An Introduction to Statistical Learning (with applications in R)” by James, Witten et al. ISBN: 978-1461471370
- Have a solid conceptual grasp on the described statistical learning methods.
- Be able to correctly identify the appropriate techniques to deal with particular data sets.
- Have a working knowledge of R programming software in order to apply those techniques and subse- quently assess the quality of fitted models.
- Demonstrate the ability to clearly communicate the results of applying selected statistical learning methods to the data.
- Review: Task of Statistical Learning. Supervised and unsupervised learning. Most ubiquitous statistical learning techniques.
- Support Vector Classifier. Maximal margin classifier: separating hyperplane, support vectors. Non-separable case: support vector classifier.
- Support Vector Machines. Non-linear decision boundaries. Kernels. One-versus-one and one-vs-all classification for K > 2 classes. Evaluating quality of classification.
- Clustering Methods: K-Means. Within-cluster variation. Computing centroids. Multiple starts. Selecting K.
- Clustering Methods: Hierarchical. Agglomerative clustering. Linkage. Interpreting dendrogram. Choice of dissimilarity measure. Data scaling.
- Evaluation of Clustering Solution. Is this a good clustering? Variance explained. Between- and within-cluster variation. Silhouette coefficient.
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