Supervised, unsupervised, and semi-supervised machine learning. Classification, regression, clustering and generative models. Data analysis foundations including data matrix from algebraic and probabilistic view, numeric attributes, graph data, high dimensional data and dimensionality reduction, experimental setups, and quantitative metrics. Algorithms: traditional machine learning (e.g., random forests), neural networks, and deep learning. Hands-on industrial applications including signal classification, de-noising, anomaly detection, and predictive analytics.
This course may not be repeated for credit.

Sections

This course will be offered next in Winter 2024.