MIS41120

Broadly speaking, we think of Statistical and Machine Learning as computational methods that use (learn from) experience to improve performance or prediction accuracy. They arose in different research communities but have significant overlap. Statistical Learning focusses more on linear models, for which there is stronger theoretical foundation, and (to an extent) on inference; Machine Learning focusses more on nonlinear methods, founded more on experimental evidence, and is often more associated with prediction.
This Statistical Learning course discusses these, and also investigates the foundations of these methods: how well they work, error estimates, tradeoffs involved, etc: the principles underpinning algorithmic learning - the methods used in Knowledge Discovery and Data Mining.
Statistical learning refers to supervised and unsupervised learning, especially regression, classification, clustering, and especially with structured numerical data. These are the most common techniques used for modelling, with the goals of inference and prediction in business (and elsewhere); hence, their statistical theory is well-developed.
This module aims to develop both theory and practice to expert level.