Detailed Course Information

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STAT 308 - Statistical Learning
An introduction to statistical learning methods with a primary focus on supervised learning. Beginning with a review of normal and generalized linear models for classification and regression problems, further topics explored may include k-nearest neighbors, cross-validation, model selection and regularization, imputation methods, non-linear regression, tree-based methods, imbalanced learning, bagging and boosting, neural networks, and deep learning. A brief introduction to unsupervised learning methods such as principal components and cluster analysis is given. Emphasis is placed on testing and training accurate predictive models for various scenarios whilst overcoming any nuances in the data. The use of statistical software such as R/RStudio is explored in order to analyze large datasets arising from a variety of domain areas. Note: Students cannot receive credit for both this course and STAT 308 Applied Statistical Data Processing. Prerequisite: STAT 307 Intermediate Statistics. 1.000 Credit hours 4.000 Lecture hours Levels: Non-Matriculated, Undergraduate Schedule Types: Directed Study, Independent/Directed Study, Lecture Mathematics Department Course Attributes: Undergraduate Level Course Restrictions: May not be enrolled in one of the following Levels: Non-Matriculated May not be assigned one of the following Student Attributes: DGCE Student Prerequisites: PREREQ for STAT 308 General Requirements: ( Course or Test: STAT 307 May not be taken concurrently. ) or ( Course or Test: MATH 307 May not be taken concurrently. ) or ( May not be taken concurrently. ) or ( May not be taken concurrently. )

STAT 308 - Statistical Learning

An introduction to statistical learning methods with a primary focus on supervised learning. Beginning with a review of normal and generalized linear models for classification and regression problems, further topics explored may include k-nearest neighbors, cross-validation, model selection and regularization, imputation methods, non-linear regression, tree-based methods, imbalanced learning, bagging and boosting, neural networks, and deep learning. A brief introduction to unsupervised learning methods such as principal components and cluster analysis is given. Emphasis is placed on testing and training accurate predictive models for various scenarios whilst overcoming any nuances in the data. The use of statistical software such as R/RStudio is explored in order to analyze large datasets arising from a variety of domain areas. Note: Students cannot receive credit for both this course and STAT 308 Applied Statistical Data Processing. Prerequisite: STAT 307 Intermediate Statistics.
1.000 Credit hours
4.000 Lecture hours

Levels: Non-Matriculated, Undergraduate
Schedule Types: Directed Study, Independent/Directed Study, Lecture

Mathematics Department

Course Attributes:
Undergraduate Level Course

Restrictions:
May not be enrolled in one of the following Levels:
Non-Matriculated
May not be assigned one of the following Student Attributes:
DGCE Student

Prerequisites:
PREREQ for STAT 308

General Requirements:
( Course or Test: STAT 307
May not be taken concurrently. )
or
( Course or Test: MATH 307
May not be taken concurrently. )
or
(
May not be taken concurrently. )
or
(
May not be taken concurrently. )

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Framingham State University

Detailed Course Information

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