QTM3635 Quantitative Methods for Machine Learning
4 Advanced Liberal Arts Credits
The ease of data collection coupled with plummeting data storage costs over the last decades have resulted in massive amounts of data that many business organizations have at their fingertips. Effective analysis of those data followed by sound decision-making is what makes a company an analytical competitor. This course is dedicated to learning and applying advanced quantitative tools for solving complex machine learning problems. The course will build on analytical tools learned during AQM 2000 (Predictive Business Analytics) course, introducing modern advanced tools ranging from random forests to support vector machines and artificial neural networks. Each topic covered in this course will be discussed in the context of wide-ranging real-world applications such as email spam prediction; handwritten digit recognition; topic modeling/text mining; etc. The implementation of the introduced topics will be carried out in R/RStudio.
Prerequisites: AQM2000
QTM3635: Quantitative Methods for Machine Learning
Academic Unit