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An Introduction to Statistical Machine Learning

Course #: MATH 455

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: Pre-requisite: MATH 345 and MATH 260 and CS 110 or permission of instructor

Offered in:

2023 Fall

Section Class Number Schedule/Time Instructor Location
01 4011 TuTh
4:00 - 5:15 pm
Zarringhalam,Kourosh McCormack M02-0213
Session: Regular
Class Dates: 09/05/2023 - 12/13/2023
Capacity: 15
Enrolled: 1
Status: Open
Credits: 3/3
Class Notes:
Pre Requisites: Pre-requisite: MATH 345 and MATH 260 and CS 110 or permission of instructor
Course Attributes:

2024 Fall

Section Class Number Schedule/Time Instructor Location
01 3771 TuTh
4:00 - 5:15 pm
TBD Wheatley W02-0200
Session: Regular
Class Dates: 09/03/2024 - 12/13/2024
Capacity: 25
Enrolled: 3
Status: Open
Credits: 3/3
Class Notes:
Pre Requisites: Pre-requisite: MATH 345 and MATH 260 and CS 110 or permission of instructor
Course Attributes: