Statistical Machine Learning

Graduate

This course introduces the important elements of statistical learning. Statistical learning refers to a set of tools for modelling and understanding complex datasets. It blends Statistics with methods in machine learning, and its main goal is to “learn from the data”; that is: to extract important patterns and trends from the data, and understand “what the data says”. Topics include: linear methods for regression, linear methods for classification; resampling methods, model assessment and selection, regularization; non- linear models, basis expansions; tree-based methods; support vector machines, kernel methods; unsupervised learning. Additional topics may include: matrix factorization methods; network-based machine learning; kernel smoothing methods; model inference and averaging; boosting methods; neural networks; prototype methods; ensemble learning; graphical models; high-dimensional problems. The legal, societal and ethical implications of artificial intelligence and machine learning are also discussed.