MACHINE LEARNING FOR DATA SCIENCE
This course is a practical introduction to machine learning, emphasizing when and how to apply techniques and how to interpret results. Topics covered include regression, classification, dimension reduction, clustering, decision trees, ensemble learning, and neural networks. Recommended Prerequisite(s): DSCI 101 or COMP 140 and DSCI 302 (may be taken concurrently) or COMP 330 or COMP 430 Mutually Exclusive: Cannot register for DSCI 303 if student has credit for ELEC 478 .
Prerequisites & course information
Short Title: MACHINE LEARNING FOR DS Department: Data Science Grade Mode: Standard Letter Course Type: Lecture Credit Hours: 3 Restrictions: Enrollment is limited to Undergraduate, Undergraduate Professional or Visiting Undergraduate level students. Course Level: Undergraduate Upper-Level Prerequisite(s): DSCI 301 or STAT 310 or STAT 280 or STAT 305 or ELEC 303 or PSYC 339 or SOCI 382 or SOSC 302 or BIOE 439 or ECON 307 or STAT 315 or BUSI 395
Sources
- Course catalogue ↗2026–2027