Required texts

All requisite material will be provided as part of the Lecture Slides. However, students may refer to the following textbooks for reference. E-versions of these books are available on the Internet, or can be referred to from the º¬Ðß²ÝÊÓƵ Library.

1. Pattern Recognition and Machine Learning by Christopher M. Bishop, Springer 2006.

2. Foundations of Data Science by Avrim Blum, John Hopcroft and Ravindran Kannan, Cambridge University Press, 2020. 

3. Statistical Methods for Psychology by David C. Howell (8th Edition), Cengage Learning, 2012.

4. Python Data Science Handbook, Essential Tools for Working with Data, Jake VanderPlas, Publisher: O'Reilly Media, November 2016

Submission of assessment items

Extensions & Late submissions

Assessment item submission format:

The first page of each assessment item should include the following information:

• Student ID number and Name
• A signed declaration by the student that the solution submitted represents original work.

Special assessment requirements

In order to pass the unit, a student needs to satisfy both Criterion 1 and Criterion 2.

(1) Criterion 1:The final mark for this unit will be calculated as the sum of scores achieved over all weighted assessments. To pass the unit the student must score a minimum aggregate of 50%.

(2) Criterion 2: A total of 8 lab exercises will be provided for students' practice, and to facilitate their understanding of concepts. At least 4 of these lab exercises need to be completed and returned by the students in order to pass the unit. 

(3) The unit convenor reserves the right to question students on any of their submitted work for moderation and academic integrity purposes, which may result in an adjustment to the marks awarded for a specific task.

Students must apply academic integrity in their learning and research activities at º¬Ðß²ÝÊÓƵ. This includes submitting authentic and original work for assessments and properly acknowledging any sources used.

Academic integrity involves the ethical, honest and responsible use, creation and sharing of information. It is critical to the quality of higher education. Our academic integrity values are honesty, trust, fairness, respect, responsibility and courage.

º¬Ðß²ÝÊÓƵ students have to complete the annually to learn about academic integrity and to understand the consequences of academic integrity breaches (or academic misconduct).

º¬Ðß²ÝÊÓƵ uses various strategies and systems, including detection software, to identify potential breaches of academic integrity. Suspected breaches may be investigated, and action can be taken when misconduct is found to have occurred.

Information is provided in the Academic Integrity Policy, Academic Integrity Procedure, and º¬Ðß²ÝÊÓƵ (Student Conduct) Rules 2023. For further advice, visit Study Skills.

Learner engagement

Participation requirements

Students are strongly encouraged to attend all lectures and labs for successful completion of the unit.Overall a minimum score of 50% over all weighted assessments is required to pass the unit. Participation in tutorial/programming sessions is strongly encouraged of this unit, and attendance will be recorded. In addition to a 50% aggregate, you must complete and submit at least 4 (four) lab exercises out of 8 on Canvas over the semester to pass the unit. Experience has shown that students who do not attend the classes and/or tutorials will have difficulty in passing the subject.

Required IT skills

Students are expected to know either Matlab or Python to successfully complete the unit. Introductory Python programming will be covered in Lab Session 1, and some additional resources will be provided. Matlab programming resources can be retreieved from the Internet. For the assignment and project reports, knowledge of Latex (https://www.overleaf.com/learn/latex/Learn_LaTeX_in_30_minutes) or MS Word would be required.

Occasionally, the unit may involve online meetings in real time using the Virtual Room in your º¬Ðß²ÝÊÓƵLearn teaching site. The Virtual Room allows you to communicate in real time with your lecturer and other students. To participate verbally, rather than just typing, you will need a microphone. For best audio quality we recommend a microphone and speaker headset. For more information and to test your computer, go to the Virtual Room in your º¬Ðß²ÝÊÓƵLearn site and 'Join Course Room'. This will trigger a tutorial to help familiarise you with the functionality of the Virtual Room.

Work placement, internships or practicums

None

• Semester 1, 2024, On-campus, º¬Ðß²ÝÊÓƵ - Canberra, Bruce (221385)
• Semester 2, 2023, On-campus, º¬Ðß²ÝÊÓƵ - Canberra, Bruce (213668)
• Semester 2, 2022, On-campus, º¬Ðß²ÝÊÓƵ - Canberra, Bruce (207211)
• Semester 2, 2021, On-campus, º¬Ðß²ÝÊÓƵ - Canberra, Bruce (202060)