Applied Machine Learning in Science and Engineering

3 credits · Graduate School

Supervised, unsupervised and reinforced learning methods; classification, clustering and numerical prediction models; distribution learning; artificial neural networks, deep learning, recursive and convolutional neural networks; dimension reduction, data preparation, model evaluation, prediction uncertainty quantification; learning with heterogeneous data fusion (numerical, text, and image data); learning with big data; and implementation with Python software. SPRING. [3].