IS 3330 Applied Machine Learning 3.0 Credits
Provides a practical introduction to applied machine learning (ML) with data and examples from real-world applications. Covers the full ML solutions lifecycle, including data exploration, feature engineering, model selection, hyperparameter tuning, and result interpretation. Introduces supervised and unsupervised learning, while exploring practical topics such as bias-variance tradeoffs, data leakage, and evaluation pitfalls. With widely-used Python libraries, the course applies clustering, classification, regression, and dimensionality reduction, and other ML tools to a range of problems, preparing students to study applied deep learning. Provides experience with reproducible experiments, project-based analysis, and model selection through assignments and a comprehensive term project.
Repeat Status: Not repeatable for credit
Prerequisites: (CS 1020 [Min Grade: D] or CS 1030 [Min Grade: D] or IS 1551 [Min Grade: D] or ECE 1210 [Min Grade: D] or CS 171 [Min Grade: D] or INFO 151 [Min Grade: D] or ECE 105 [Min Grade: D]) and (MATH 2801 [Min Grade: D] or STAT 2201 [Min Grade: D] or PHYS 4801 [Min Grade: D] or MATH 1801 [Min Grade: D] or ECE 3700 [Min Grade: D] or BMES 2110 [Min Grade: D] or MATH 311 [Min Grade: D] or STAT 202 [Min Grade: D] or PHYS 440 [Min Grade: D] or MATH 410 [Min Grade: D] or ECE 361 [Min Grade: D] or BMES 310 [Min Grade: D])