| Description: The field of statistical learning, which evolved out of machine learning (including neural-nets) and data-mining, focuses on finding patterns, associations, and relationships in data. In examining real-world datasets, this course highlights, develops and applies methods in simple and multiple linear and logistic regression, classification and discriminant analysis, resampling methods, model selection, additive models and splines, tree-based methods, support vector machines, and unsupervised learning techniques such as clustering and PCA. The focus throughout this course is on applications and real-life data sets; as such, theorems and proofs will not be emphasized (but can be found in provided outside references). This course is project-based in that students will apply these topics to data from real projects. |
