Applied Machine Learning with Business Applications
Luyi Yang, Professor
Business, Haas School
Applications for Spring 2024 are closed for this project.
Machine learning and data mining have been changing the business world. However, they can still be a black box to non-experts who lack technical expertise. This project aims to demystify the machine learning workflow for lay people by delivering efficient and well-explained computer code that is comprehensible to a broad audience with a minimum technical background.
Role: In this project, you will implement a wide range of supervised and unsupervised machine learning models (such as random forests and k-means clustering) in Python to identify hidden patterns in various real-world data and address important managerial questions in a host of business applications, including Instacart transactions, Obama's Tweets, Bitcoin news headlines, LendingClub loans, medical imagery, college football, Spotify hits, among others. You will gain hands-on experience with applying machine learning methods to business applications.
Qualifications: Applicants majoring in Computer Science, Data Science, Statistics, IEOR, and related quantitative fields are welcome.
Required skills/coursework: Knowledge of machine learning and experience with R and Python are required.
Hours: 3-5 hrs