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| STAT 51800 - Introduction To Statistical Learning |
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Credit Hours: 3.00. This course provides an introduction to supervised learning, with focus on regression and classification methods. Both theory and application of learning methods are emphasized. Some unsupervised learning methods are also discussed, such as principal component analysis and clustering methods. Permission of department required. Typically offered Spring.
3.000 Credit hours Syllabus Available Levels: Undergraduate, Graduate, Professional Schedule Types: Distance Learning, Lecture Offered By: Regional Campus Only Course Attributes: Upper Division May be offered at any of the following campuses: PU Fort Wayne Learning Outcomes: 1. Understand the key concepts of statistical learning: explain the types of modeling problems and methods, including supervised versus unsupervised learning, regression versus classification; explain the common methods of assessing model accuracy; employ basic methods of exploratory data analysis, including data checking and validation. 2. Understand the key concepts of model selection and validation: explain different validation approaches; explain different model selection methods; implement model selection and validation using R and interpret the results. 3. Understand the key concepts of classification problems: explain different classification methods; implement classification methods using R and interpret the result. 4. Understand the key concepts concerning decision tree models: explain the purpose and uses of decision trees; explain and interpret decision trees, considering regression trees and recursive binary splitting; explain and interpret bagging, boosting, and random forests; explain and interpret classification trees, their construction, Gini index, and entropy; compare decision trees to linear models; implement decision tree analysis using R and interpret the results. 5. Understand the key concepts of unsupervised learning methods (principal components analysis and cluster analysis): define principal components and explain uses of principal components; interpret the results of a principal components analysis, considering loading factors and proportion of variance explained; explain different clustering methods and their uses; interpret the results of cluster analysis; implement unsupervised learning methods in R. 6. Understand the key concepts of more advance learning methods such as support vector machines and neural networks: explain support vector machine classifiers; explain neural networks; implement these learning methods using R and interpret the results. |
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