University of Maryland Global Campus 3 credits ARIN
Prerequisites: DATA 230 and DATA 430. A project-based study of advanced concepts and applications in machine learning (ML), such as neural networks, support vector machines (SVM), ensemble models, deep learning, and reinforced learning. Emphasis is on building predictive models for practical business and social problems, developing complex and explainable predictive models, assessing classifiers, and comparing their performance. All stages of the ML life cycle are developed, following industry best practices for selecting methods and tools to build ML models, including Auto ML. Students may receive credit for only one of the following courses: ARIN 440 or DATA 440.
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