Results - PHYS 5336 - UTD TRENDS

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Big Data and Machine Learning for Scientific Discovery

PHYS 5336

School of Natural Sciences and Mathematics

This class introduces a wide range of machine learning techniques suitable for Big Data analysis. The techniques covered include multivariate non-linear non-parametric regression and classification, both supervised and unsupervised. These approaches are directly applicable to many issues of major scientific and societal importance. The practical tools introduced (Neural Networks, Support Vector Regression, Decision Trees, Random Forests, etc) can be readily used in a wide range of applications from research to real time decision support. The data used can come from a wide variety of sources including scientific instrumentation, social media, remote sensing, aerial vehicles, and the internet of things. 3 credit hours.

Offering Frequency: Based on student interest and instructor availability

Grades: 95

Median GPA: A

Mean GPA: 3.929

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