HADM 4750
Last Updated
- Schedule of Classes - September 10, 2024 10:17AM EDT
- Course Catalog - September 10, 2024 9:19AM EDT
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HADM 4750
Course Description
Course information provided by the Courses of Study 2023-2024.
The world is becoming increasingly data driven. In this context, the ability to leverage machine learning techniques to extract value from data is vital across many businesses, including the hospitality industry. This course is designed to meet the emerging need of this sector. This course aims to convey core principles of machine learning and hands-on applications of on solving real business problems. This course emphasizes on how to draw managerial insights and support business decisions from data. The methods that would be covered include linear regression, logistic regression, classification trees, clustering, and neural networks. This course also explains concepts including bias-variance trade-off, model interpretability, cross-validation, prescriptive analytics, and ethical concerns of machine learning.
When Offered Spring.
Permission Note Priority given to: SHA students.
Prerequisites/Corequisites Prerequisite: HADM 2010 or HADM 2011 or HADM 2021 or HADM 3010.
Satisfies Requirement Elective.
Comments Course can qualify for Hospitality Analytics Specialization elective.
Outcomes- Understanding of the basics concept and pipeline of machine learning.
- Apply and interpret the outcome of popular machine learning algorithms.
- Using data to support decisions.
- Be aware of ethical concerns of machine learning, including fairness, privacy, security issues and social responsibility.
Seven Week - Second. Combined with: HADM 6750
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Credits and Grading Basis
1.5 Credits Stdnt Opt(Letter or S/U grades)
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Class Number & Section Details
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Meeting Pattern
- TR Statler Hall 445
- Mar 13 - May 7, 2024
Instructors
Qi, M
Seven Week - Second. Combined with: HADM 6750
-
Credits and Grading Basis
1.5 Credits Stdnt Opt(Letter or S/U grades)
-
Class Number & Section Details
-
Meeting Pattern
- TR Statler Hall 391
- Mar 13 - May 7, 2024
Instructors
Qi, M
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