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Applied Machine Learning

Course #: CS 638

Description:
This course presents the practical side of machine learning for applications, such as pattern recognition from images or building predictive classifiers. Topics will include linear models for regression, decision trees, rule based classification, support vector machines, Bayesian networks, and clustering. The emphasis of the course will be on the hands-on application of machine learning to a variety of problems.

Notes:
This course does not assume any prior exposure to machine learning theory or practice.

Pre Requisites:

Offered in:

2023 Fall

Section Class Number Schedule/Time Instructor Location
01 3852 MW
4:00 - 5:15 pm
Babur,Ozgun McCormack M01-0409
Session: Regular
Class Dates: 09/05/2023 - 12/13/2023
Capacity: 17
Enrolled: 16
Status: Open
Credits: 3/3
Class Notes:
Pre Requisites:
Course Attributes:

2024 Fall

Section Class Number Schedule/Time Instructor Location
01 3633 MW
4:00 - 5:15 pm
Babur,Ozgun McCormack M02-0208
Session: Regular
Class Dates: 09/03/2024 - 12/13/2024
Capacity: 17
Enrolled: 0
Status: Open
Credits: 3/3
Class Notes:
Pre Requisites:
Course Attributes:

2024 Spring

Section Class Number Schedule/Time Instructor Location
01 10069 MW
5:30 - 6:45 pm
Ding,Wei Wheatley W01-0005
Session: Regular
Class Dates: 01/22/2024 - 05/08/2024
Capacity: 28
Enrolled: 25
Status: Open
Credits: 3/3
Class Notes:
Pre Requisites:
Course Attributes: