Applied Machine Learning for Industrial Engineers.

Catalog description

Introduction to machine learning methods and tools tailored for industrial engineering graduates. Supervised and unsupervised learning techniques, including regression, classification, clustering, and forecasting. Fundamental concepts such as bias-variance trade-offs, model evaluation, resampling methods, regularization techniques, and tree-based models. Modern deep learning architectures, including convolutional and recurrent neural networks.