Machine Learning for Data Scientists

Catalog description

Covers essential statistical and machine learning methods, including regression, classification, resampling, ensemble methods, boosting, clustering, and foundational algorithms. Students apply these techniques to real-world data using modern frameworks, gaining practical skills and foundational knowledge in both classical and modern approaches, preparing for advanced study in data science. Course overlaps with: TCSS 455; CSE 416/STAT 416; and E E 344. Prerequisite: a minimum 2.0 grade in TSTAT 280; and a minimum 2.0 grade in either TMATH 126 or MATH 126.