3.00 credit hours
Advances in computational analytics including Machine, Deep and Statistical Learning (ML) provide powerful methods for developing mathematical learning models that can autonomously parse, learn from, and make predictions from data to improve performance with experience. In deep learning, large neural networks are leveraged to achieve artificial intelligence (AI), enabling machines to mimic human behavior. This course covers principles, algorithms, and applications of machine learning from a business analytics perspective. Specifically, the course will provide a practical understanding of modern machine learning techniques including regression and classification methods, resampling methods and model selection, regularization, perceptron and artificial neural networks, tree-based methods, support vector machines and kernel methods, and grouping methods.
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