IS 5330 Applied Machine Learning 3.0 Credits
Offers an in-depth, practice-oriented study of applied machine learning (ML) and modern data analysis. Covers the full ML lifecycle, including data exploration, feature engineering, model selection, hyperparameter tuning, error analysis, and result interpretation. Introduces and explores practical topics, such as bias-variance tradeoffs, data leakage, and evaluation pitfalls. Using Python and widely-used libraries, the course emphasizes applying and comparing clustering, classification, regression, and other ML tasks to real-world problems, preparing students to study applied deep learning. Graduate students will analyze methodological trade-offs, interpret results with statistical and algorithmic rigor, and complete a comprehensive project demonstrating end-to-end ML problem solving.
Repeat Status: Not repeatable for credit
Prerequisites: IS 5320 [Min Grade: D], CS 5030 [Min Grade: D] (Can be taken Concurrently) or (IS 5310 [Min Grade: D] or CS 5010 [Min Grade: D] or DSCI 511 [Min Grade: D] or CS 501 [Min Grade: D]) or DSCI 501 [Min Grade: D] or CS 502 [Min Grade: D])