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DSCI 631 Applied Machine Learning for Data Science 3.0 Credits

Introduces relevant topics in the life cycle of machine learning: extracting and engineering features, tuning parameters, comparing algorithms, interpreting results, and analyzing errors. Students will be exposed to various representative algorithms in the concept level and learn their trade-offs. Students will gain hands-on experiences with assignments and a term project. Students will be prepared to attack new problems using various machine learning methods and be able to compare the performance of different algorithms for the term project.

College/Department: College of Computing and Informatics
Repeat Status: Not repeatable for credit
Prerequisites: DSCI 521 [Min Grade: C] (Can be taken Concurrently)

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