University of Technology Sydney

37401 Machine Learning: Mathematical Theory and Applications

Warning: The information on this page is indicative. The subject outline for a particular session, location and mode of offering is the authoritative source of all information about the subject for that offering. Required texts, recommended texts and references in particular are likely to change. Students will be provided with a subject outline once they enrol in the subject.

Subject handbook information prior to 2022 is available in the Archives.

UTS: Science: Mathematical and Physical Sciences
Credit points: 8 cp
Result type: Grade and marks

There are course requisites for this subject. See access conditions.

Description

This subject introduces several machine-learning techniques and the most important mathematical foundations required in machine learning. The topics covered include time series prediction, neural networks, genetic algorithms and reinforcement learning.

The subject combines the required theory with practical applications to problems arising in statistics, data analysis and quantitative finance including algorithmic trading, portfolio optimisation and risk management.