Studying online

There are now 2 possible online modes for units:

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

Click on an offering mode for more details.

Unit Overview


In this unit, students will develop an understanding of machine learning techniques that are applicable to both scientific and business data. Extracting meaningful knowledge from large amounts of data has become a priority for businesses and scientific domains. Machine learning provides core underlying theory and techniques to data analytics, where algorithms iteratively learn from data to uncover hidden insights. This unit, covers techniques of classical supervised and unsupervised machine learning.

6 points
(see Timetable)
Semester 1UWA (Perth)Face to face

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; and (4) produce practical implementations of machine learning solutions for a real-world dataset.


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 Debora Correa
Unit rules
Enrolment in
HON-CMSSE Computer Science and Software Engineering
or 62510 Master of Information Technology
or 62530 Master of Data Science
or 42630 Master of Business Analytics
or 62550 Master of Professional Engineering
or 53560 Master of Physics
or BH008 Bachelor of Advanced Computer Science [Honours]
or 73660 Master of Medical Physics
or ( Bachelor of Engineering (Honours) or an associated Combined Degree
and 96 points
and Successful completion of
CITS1401 Computational Thinking with Python
or CITX1401 Computational Thinking with Python
or BUSN5101 Programming for Business
or CITS2401 Computer Analysis and Visualisation
Advisable prior study
Linear algebra (eg MATH1012) and computer programming (eg CITS4009)
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.
  • Contact hours provide an indication of the type and extent of in-class activities this unit may contain. The total amount of student work (including contact hours, assessment time, and self-study) will approximate 150 hours per 6 credit points.