ML & Graphic Computing Fnd

Catalog description

This course introduces machine learning (ML) and graphic computing at the intersection of intelligent algorithms and visual modeling. It covers the techniques that enable machine to learn patterns and make predictions from data. Topics include supervised and unsupervised learning, classification, regression, clustering, model evaluation, and overfitting. Students will gain hands-on experience building ML graphic models using PyTorch, Scikit-Learn, and tools for neural networks.