Studying online

There are now 3 possible online modes for units:

Units with modes Online timetabled and Online flexible are available for any student to self-enrol and study online.

Units available in Online Restricted mode have been adapted for online study only for those students who require the unit to complete their studies and who are unable to attend campus owing to exceptional circumstances beyond their control. To be enrolled in a unit in Online Restricted mode, students should contact their Student Advising Office through askUWA

Click on an offering mode for more details.

Unit Overview

Description

There is an explosion in data generation and data collection due to improvements in sensing technologies and business processes. Extracting meaningful knowledge from large amounts of data has become a priority for businesses as well as scientific domains. Machine learning provides core underlying theory and techniques to data analytics, where algorithms iteratively learn from data to uncover hidden insights. In this unit, students will develop in-depth understanding of machine learning techniques that are applicable to both scientific and business data. The topics covered by the unit include supervised classification, unsupervised classification, regression, support vector machines, decision trees, random forests, dimensionality reduction, artificial neural networks, deep neural networks, autoencoders, and reinforcement learning.

Credit
6 points
Offering
(see Timetable)
AvailabilityLocationMode
Semester 1UWA (Perth)Face to face
Semester 1OnlineOnline Restricted
Outcomes

Students are able to (1) explain the role of machine learning in knowledge extraction; (2) explain the difference between supervised and unsupervised learning algorithms; (3) demonstrate a systematic knowledge of algorithmic machine learning approaches; (4) produce practical implementations of machine learning solution for a real-world dataset; (5) analyse data datasets from the perspective of machine learning; and (6) evaluate what deep learning is, what makes it work or fail, and critique where it should be applied.

Assessment

Indicative assessments in this unit are as follows: (1) mid-semester test; (2) assessed laboratory exercises; and (3) final examination. Further information is available in the unit outline.

Student may be offered supplementary assessment in this unit if they meet the eligibility criteria.

Unit Coordinator(s)
Dr Du Huynh
Unit rules
Prerequisites
Enrolment in
Computer Science and Software Engineering [Honours]
or 62510 Master of Information Technology
or 62530 Master of Data Science
or 63550 Master of Engineering
or 42630 Master of Business Analytics
or 62550 Master of Professional Engineering
or 53560 Master of Physics (specialised in Medical Physics)
or BH008 Bachelor of Advanced Computer Science [Honours] and the MJD-CDSDM Computing and Data Science major
or the MJD-ARIDM Artificial Intelligence major
) and completion of 12 points of programming-based units
Advisable prior study
CITS1401 Computational Thinking with Python (ID 411) , and Calculus
Contact hours
lectures: 2 hours per week
labs: 2 hours per week for 11 weeks from week 2
  • The availability of units in Semester 1, 2, etc. was correct at the time of publication but may be subject to change.
  • All students are responsible for identifying when they need assistance to improve their academic learning, research, English language and numeracy skills; seeking out the services and resources available to help them; and applying what they learn. Students are encouraged to register for free online support through GETSmart; to help themselves to the extensive range of resources on UWA's STUDYSmarter website; and to participate in WRITESmart and (ma+hs)Smart drop-ins and workshops.
  • Unit readings, including any essential textbooks, are listed in the unit outline for each unit, one week prior the commencement of study. The unit outline will be available via the LMS and the UWA Handbook one week prior the commencement of study. Reading lists and essential textbooks are subject to change each semester. Information on essential textbooks will also be made available on the Essential Textbooks. This website is updated regularly in the lead up to semester so content may change. It is recommended that students purchase essential textbooks for convenience due to the frequency with which they will be required during the unit. A limited number of textbooks will be made available from the Library in print and will also be made available online wherever possible. Essential textbooks can be purchased from the commercial vendors to secure the best deal. The Student Guild can provide assistance on where to purchase books if required. Books can be purchased second hand at the Guild Secondhand bookshop (second floor, Guild Village), which is located on campus.