HADM 6750 - Machine Learning for Business and Hospitality Applications

Spring. 1.5 credits. Student option grading.

Prerequisite: some coursework in basic statistics and quantitative classes, including HADM 2010/2011, HADM 3010, or equivalent classes. Basic computing classes, including HADM 1740/HADM 2740 or equivalent classes. Introduction level of Python programming required, for example HADM 3710/6710. Priority given to: SHA students. Course can qualify for Hospitality Analytics Specialization elective. Elective. Co-meets with HADM 4750.

M. Qi.

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.

Outcome 1: Understanding of the basics concept and pipeline of machine learning.

Outcome 2: Apply and interpret the outcome of popular machine learning algorithms.

Outcome 3: Using data to support decisions.

Outcome 4: Be aware of ethical concerns of machine learning, including fairness, privacy, security issues and social responsibility.