Program: MSIS/PhD
Catalog Description
Large datasets are increasingly becoming available across many sectors such as healthcare, energy, and online markets. This course focuses on methods that allow “learning” from such datasets to uncover underlying relationships and patterns in the data, with a focus on predictive performance of various models that can be built to represent the underlying function generating the data. Topics to be covered: Linear Regression, Classification, Resampling Methods, Linear Model Selection and Regularization, Tree-Based Methods, Support Vector Machines, Unsupervised Learning (Clustering).
Prerequisites
Graduate standing.
Scheduled and Upcoming Classes for this Course
Class Name | Semester | Day(s) | Start Time(s) | End Time(s) | Building | Room |
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INF 397: Research in Information Studies: Introduction to Machine Learning / Statistical Analysis and Learning
Varun Rai |
Fall Term 2025 |
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INF 397: Research in Information Studies: Introduction to Machine Learning / Statistical Analysis and Learning
Varun Rai |
Spring Term 2025 |
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Past Classes for this Course
Class Name | Semester | Day(s) | Start Time(s) | End Time(s) | Building | Room |
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INF 397: Research in Information Studies: Introduction to Machine Learning / Statistical Analysis and Learning
Varun Rai Syllabus |
Spring Term 2024 |
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INF 397: Research in Information Studies: Introduction to Machine Learning / Statistical Analysis and Learning
Varun Rai Syllabus |
Fall Term 2024 |
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INF 397: Research in Information Studies: Introduction to Machine Learning / Statistical Analysis and Learning
Varun Rai Syllabus |
Fall Term 2023 |
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