Machine Learning for Engineers

Catalog description

Introduction to machine learning and applications. Neural network (NN) models and structures. Training, validation and test data pre-processing and scaling. Universal approximation theorem. Error Backpropagation. Optimization and training algorithms. NN model utilization for design. Diversity and potential of engineering applications. Practical considerations of machine learning. Prerequisite: MAT 149 Engineering Mathematics