Applied Machine Learning with Business Applications
Luyi Yang, Professor
Business, Haas School
Closed. This professor is continuing with Spring 2024 apprentices on this project; no new apprentices needed for Fall 2024.
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