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Overview

This course is offered in two modes: either face to face (on-campus) and fully online. This will be an advanced course on machine learning for health data scientists.  This course will cover advanced contemporary machine learning algorithms and methods. Along with the theory, this course will cover a range of … For more content click the Read More button below. This course is designed to build upon the content of HDAT9500 by further progressing the knowledge in the theory, technologies and solutions currently needed by Health Data Scientists working in the area of applied machine learning.

Delivery

Multimodal - Standard (usually weekly or fortnightly)

Please note that the above delivery information is an indication on how this course may be delivered within the year.  Delivery mode(s) for each term can be viewed on other sources such as myUNSW and class timetable site.

This Handbook is a comprehensive catalogue of our offerings and includes courses that can be taken to satisfy program requirements irrespective as to their availability for a particular year. Availability of courses is best checked using filters on this site or on the class timetable site.

Fees

TypeAmount
Commonwealth Supported Students (if applicable)$1038
Domestic Students$4170
International Students$5250

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Note that the University reserves the right to vary student fees in line with relevant legislation. The fee information is provided as a guide and more specific information about fees, including policy, can be found here.

Commonwealth Supported Students:

Please refer to the information about new Student Contribution band classifications. Note that grandfathering arrangements are applicable for some cohorts of students, and other special arrangements apply for a limited number of programs. The Field of Education displayed on the right-hand side of this page may be applicable in understanding which fee rate will apply to you.

Where a Commonwealth Supported fee is displayed it does not guarantee that such places are available.

Pre-2019 Handbook Editions

Access past handbook editions (2018 and prior)
Pre-2019 Handbook Editions