CITS5508 Machine Learning

Credit
6 points
Offering
(see Timetable)
AvailabilityLocationMode
Semester 1UWA (Perth)Face to face
Content
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.
Outcomes
Students are able to (1) understand the role of machine learning in knowledge extraction; (2) understand the difference between supervised and unsupervised learning algorithms; (3) display a systematic knowledge of algorithmic machine learning approaches; (4) produce practical implementations of machine learning solution for a real-world dataset; (5) develop the ability to analyse data datasets from the perspective of machine learning; and (6) assess 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) programming exercises; and (3) final examination. Further information is available in the unit outline.

Supplementary assessment is only available in this unit in the case of a student who has obtained a mark of 45 to 49 and is currently enrolled in this unit, and it is the only remaining unit that the student must pass in order to complete their course.
Unit Coordinator(s)
Dr Du Huynh
Unit rules
Prerequisites:
enrolment in the Master of Data Science
or
the Master of Information Technology
or
the Master of Professional Engineering (Software Engineering)
or
the Computer Science and Software Engineering [Honours] and completion of 12 points of programming-based units.
Contact hours
lectures: 2 hours per week; labs: 2 hours per week for 11 weeks from week 2
Unit Outline
Semester 1_2019 [SEM-1_2019]
  • 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.
  • Books and other material wherever listed may be subject to change. Book lists relating to 'Preliminary reading', 'Recommended reading' and 'Textbooks' are, in most cases, available at the University Co-operative Bookshop (from early January) and appropriate administrative offices for students to consult. Where texts are listed in the unit description above, an asterisk (*) indicates that the book is available in paperback.