An Introduction to Statistical Machine Learning
Course #: MATH 655
Description:
This course will provide an introduction to methods in statistical machine learning that are commonly used to extract important patterns and information from data. Topics include: supervised and unsupervised learning algorithms such as generalized linear models for regression and classification, support vector machines, random forests, k-means clustering, principal component analysis, and the basics of neural networks. Model selection, cross-validation, regularization, and statistical model assessment will also be discussed. The topics and their applications will be illustrated using the statistical programming language R in a practical, example/project oriented manner.
Pre Requisites:
Offered in:
2024 Fall
| Section | Class Number | Schedule/Time | Instructor | Location | |
|---|---|---|---|---|---|
| 01 | 3772 | TuTh 4:00 - 5:15 pm |
Degras-Valabregue,David Abel | University Hall Y04-4100 | |
|
Session:
Regular
Class Dates:
09/03/2024 - 12/13/2024
Capacity:
5
Enrolled:
2
Status:
Open
Credits:
4/4
Class Notes:
Pre Requisites:
Course Attributes:
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