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About CS 5540 A

Provides a broad introduction to machine learning and statistical pattern recognition. Topics include: supervised learning (linear regression, logistic regression, neural networks, support vector machines, decision tree, ensemble models, random forest); unsupervised learning (clustering, dimensionality reduction, kernel methods); Also introduces deep learning such as convolutional neural networks and discusses recent applications. Credit not awarded for more than one of the following: CSYS 5540, CS 5540, CS 3540. Prerequisites: Knowledge of statistics as from STAT 2510, knowledge of linear algebra as from MATH 2522 or MATH 2544; Graduate student. Cross-listed with: CSYS 5540.

Notes

Co-located with CS 3540 A and CMPE 3990 B; Cross-listed with CSYS 5540 A; Total combined enrollment: 45; Open to Degree and PACE students; Prereqs enforced by the system: STAT 2510, MATH 2522 or 2544

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