Statistical Machine Learning (COMP90051)

AIMS

With exponential increases in the amount of data becoming available in fields such as finance and biology, and on the web, there is an ever-greater need for methods to detect interesting patterns in that data, and classify novel data points based on curated data sets. Learning techniques provide the means to perform this analysis automatically, and in doing so to enhance understanding of general processes or to predict future events.

Topics covered will include: supervised learning, semi-supervised and active learning, unsupervised learning, kernel methods, probabilistic graphical models, classifier combination, neural networks.

This subject is intended to introduce graduate students to machine learning though a mixture of theoretical methods and hands-on practical experience in applying those methods to real-world problems.

INDICATIVE CONTENT

Topics covered will include: linear models, support vector machines, random forests, AdaBoost, stacking, query-by-committee, multiview learning, deep neural networks, un/directed probabilistic graphical models (Bayes nets and Markov random fields), hidden Markov models, principal components analysis, kernel methods.