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Fisk University · 3 credits · UGRD

This course provides an introduction to machine learning (ML) for social sciences and all majors. Topics include (but not limited to): understand ML algorithms, models, and core ML concepts; classify examples with classifiers, and quantify examples with regressors; realistically assess performance of machine learning systems; use feature engineering to smooth rough data into useful forms; chain multiple components into one system and tune its performance; apply machine learning techniques to images and text; connect the core concepts to neural networks and graphical models; leverage from software libraries and other powerful tools; and review case studies in order to investigate cultural bias in machine learning models and software tools.
Fall 2026 · 1 section
Aug 17 – Dec 11
| Section | Days & time | Location | Instructor |
|---|---|---|---|
| 01Class #6300-CSCI-390M-01 | Tuesday, Thursday 1:00 PM - 2:20 PM Library 321 (Lecture) | Library 321 | Hina Raja |