UO Machine Learning

Course aim

To develop skills in applying supervised, unsupervised and deep machine learning techniques to real-world data analytics problems.

Course content

Linear regression, regularization, logistic regression, neural network architectures, neural network learning, convolutional neural network, recurrent neural network, long short-term memory, machine learning in practise, clustering, dimensionality reduction, anomaly detection, large-scale machine learning.

Textbooks

Nil

Prerequisites

Subject Area & Catalogue Number	Course Name
Group 1 Students must have completed one of the following courses:
INFS 3081	UO Predictive Analytics
INFS 3076	Predictive and Descriptive Analytics

Corequisite(s)

Nil

Teaching Method

Component	Duration
EXTERNAL, ONLINE ACTIVITY
Online	10 weeks x N/A

Note: These components may or may not be scheduled in every study period. Please refer to the timetable for further details.

Assessment

Programming exercise, Project

Fees

EFTSL*: 0.125
Commonwealth Supported program (Band 2)
To determine the fee for this course as part of a Commonwealth Supported program, go to:
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Fee-paying program for domestic and international students
International students and students undertaking this course as part of a postgraduate fee paying program must refer to the relevant program home page to determine the cost for undertaking this course.

Non-award enrolment
Non-award tuition fees are set by the university. To determine the cost of this course, go to:
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Not all courses are available on all of the above bases, and students must check to ensure that they are permitted to enrol in a particular course.

* Equivalent Full Time Study Load. Please note all EFTSL values are published and calculated at ten decimal places. Values are displayed to three decimal places for ease of interpretation