MACHINE LEARNING.

Catalog description

Pre-requisites: CSCD 300 with a grade ≥C+, Advancement Programming Exam Clearance. Corequisite: CSCD 484L . Study of various methods for learning and recognizing patterns in data. Discusses supervised learning models that include decision trees, linear regression and its nonlinear transformation, logistic regression, neural networks, and model ensembles. Topics include various clustering algorithms and principle component analysis for dimensionality reduction. Focuses on explaining why these underhood learnings can be possible and how they are achieved. Companion course to CSCD 484L .