Results - CE 6364 - UTD TRENDS

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Machine Learning and Pattern Recognition

CE 6364

Erik Jonsson School of Engineering and Computer Science

This course covers basic concepts and algorithms for pattern recognition and machine learning. Bayesian decision theory, parametric learning, non-parametric learning, linear regression, linear classifiers and support vector machine, kernel methods, data clustering, mixture models, component analysis, multilayer neural networks, and deep learning with convolutional neural networks. 3 credit hours.

Prerequisites: Knowledge of probability and knowledge of MATLAB or C.

Offering Frequency: Every two years

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