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| STAT 51800 - Introduction To Statistical Learning |
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Associated Term:
Spring 2020
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. Required Materials: Technical Requirements: |
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