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ECE 461P

Data Science Principles

4 Hours

Examine principles of unsupervised and supervised learning; exploratory data analysis; feature engineering; predictive modeling for regression and classification; clustering algorithms; neural networks and stochastic gradient descent methods; scalable models for Big Data sets; case studies; and programming predictive models in Python and R. Only one of following may be counted: Electrical and Computer Engineering 461P, Electrical Engineering 361M, 461P. Three lecture hours and one recitation hour a week for one semester.

Prerequisites & course information

Examine principles of unsupervised and supervised learning; exploratory data analysis; feature engineering; predictive modeling for regression and classification; clustering algorithms; neural networks and stochastic gradient descent methods; scalable models for Big Data sets; case studies; and programming predictive models in Python and R. Only one of following may be counted: Electrical and Computer Engineering 461P, Electrical Engineering 361M, 461P. Three lecture hours and one recitation hour a week for one semester.

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