The University of Alabama 3 credits PH
The course will cover a mixture of foundational and applied machine learning topics related to practical applications in analysis of large scientific data. Students will learn the theory behind various machine learning algorithms and tools and will learn how to apply them to real-world problems. This course will introduce the fundamentals of machine learning and classification theory based on statistical learning and describe classes of popular algorithms in depth: decision and rule-based methods (decision trees and rules, bagging and boosting, random forests), deep learning-based models (fully connected, convolutional, recurrent, recursive, Bayesian, geometric deep learning and graph neural networks) as well as other machine learning algorithms. The lectures will be augmented by active learning techniques to promote greater and deeper student engagement. There will be various in-class activities and small-group discussions and problem solving to allow students to build and reinforce connections with fellow students.
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