Pattern Recognition and Machine Learning PG (11512.1)

Please note these are the 2024 details for this unit
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Pattern Recognition and Machine Learning PG
Available teaching periods Delivery mode Location
View teaching periods On-Campus
 Bruce, Canberra
EFTSL Credit points Faculty
0.125 3 Faculty Of Science And Technology
Discipline Study level HECS Bands
Academic Program Area - Technology Post Graduate Level Band 2 2021 (Commenced After 1 Jan 2021)
Band 3 2021 (Commenced Before 1 Jan 2021)
Pattern recognition, machine learning and deep learning are closely related topics in the field of artificial intelligence (AI) and its applications in robotics, computer vision, natural language processing, data science, and many others. This unit is an advanced unit in artificial intelligence that focuses on the core element of modelling and recognising patterns in data through learning. Application areas are in analysing data from images and video, healthcare, finance, sports, text documents, speech, human-machine interaction and many more. This unit covers selected topics from Bayesian Inference, Deep Neural Networks, Support Vector Machines/Regression, Graphical Models, and Mixture Models as well as ethical and privacy considerations around the use of AI. Students will gain both an understanding of the theoretical foundations as well as hands-on experience in implementing and using machine learning techniques in real-world applications.

This unit will be co-taught with 11482 Pattern Recognition and Machine Learning.

Learning outcomes

After successful completion of this unit, students will be able to:
1. Understand, describe and critique advanced pattern recognition, machine learning and deep learning techniques;
2. Identify and select suitable modelling, learning and prediction techniques to solve a complex data problem;
3. Design and implement a refined machine learning solution; and
4. Appraise ethical and privacy issues of artificial intelligence techniques.


Prerequisites

None.

Corequisites

11516 Introduction to Data Science G OR 8936 Introduction to Information Technology G

Incompatible units

11482 Pattern Recognition and Machine Learning

Equivalent units

None.

Assumed knowledge

Working knowledge of programming (e.g. scripting languages), discrete mathematics, algebra and numerical analysis.
Year Location Teaching period Teaching start date Delivery mode Unit convener
2024 Bruce, Canberra Semester 2 29 July 2024 On-Campus Prof Dharmendra Sharma
The information provided should be used as a guide only. Timetables may not be finalised until week 2 of the teaching period and are subject to change. Search for the unit timetable.

Required texts

No required textbook. Students can consult any on-line or library reference material on the relevant topics. Some useful links are suggested below.

Reference Material Recommended:

  1. Pattern Classification (2nd. Edition) by R. O. Duda, P. E. Hart and D. Stork, Wiley 2002,
  2. Hands-On Machine Learning with Scikit-Learn and TensorFlowby, Aurélien Géron, O'Reilly Media, 2017,
  3. Pattern Recognition and Machine Learning by C. Bishop, Springer 2006,
  4. Deep Learning by Ian Goodfellow, Yoshua Bengio and Aaron Courville,MIT Press 2016,
  5. Artificial intelligence: a modern approach by Russell, S. J., Norvig, P., & Davis, E. (2010. 3rd ed.) Upper Saddle River, NJ: Prentice Hall. http://aima.cs.berkeley.edu/
  6. Statistics and the Evaluation of Evidence for Forensic Scientists by C. Aitken and F. Taroni, Wiley, 2004,
  7. Python Data Science Handbook, Essential Tools for Working with Data, Jake VanderPlas, Publisher: O'Reilly Media, November 2016, and
  8. Introduction to Machine Learning with Python, Andreas C. Müller and Sarah Guido, A Guide for Data Scientists, Andreas C. Müller and Sarah Guido -  O'Reilly, 2016

Submission of assessment items

Special assessment requirements

In PRML, all the assessment items are to be completed either individually or in groups of 2. Students are required to satisfactorily complete all the assessment requirements of the unit. To obtain a particular grade in this unit it is necessary that there are no outstanding submissions at the end of week 14. The unit convener reserves the right to question students orally on any of their submitted work.

All assessment items will receive a numerical mark. The final grade will be determined as a weighted average of the individual assessment items.

To be awarded a particular grade in PRML, students must meet the overall requirements, the assignment requirements and the exam requirements set out in the table below. All grades are conditional upon the following minimum requirements:

Minimum 50% of available marks in all assessment tasks.

Grade

Assignments + Exam

Pass

Minimum 50% of combined weighted marks of all assessment items

Credit

Minimum 65% of combined weighted marks of all assessment items

Distinction

Minimum 75% of combined weighted marks of all assessment items

High Distinction

Minimum 85% of combined weighted marks of all assessment items

Note: 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.

Academic integrity

Students have a responsibility to uphold University standards on ethical scholarship. Good scholarship involves building on the work of others and use of others' work must be acknowledged with proper attribution made. Cheating, plagiarism, and falsification of data are dishonest practices that contravene academic values. Refer to the University's Student Charter for more information.

To enhance understanding of academic integrity, all students are expected to complete the Academic Integrity Module (AIM) at least once during their course of study. You can access this module within UCLearn (Canvas) through the 'Academic Integrity and Avoiding Plagiarism' link in the Study Help site.

Use of Text-Matching Software

The University of Canberra uses text-matching software to help students and staff reduce plagiarism and improve understanding of academic integrity. The software matches submitted text in student assignments against material from various sources: the internet, published books and journals, and previously submitted student texts.

Learner engagement

Activity

Unit Workload (hrs) - as a guide

Lectures Attendance (1.5 hrs x 12 weeks)

18

Lectures Preparation (1.5 hrs x 12 weeks) 

18

Tutorials Attendance (2 hr x 11 weeks)

22

Assignment 1 – Paper/Project Critique

25

Assignment 2 – Project Proposal

25

Assignment 3 – Unit Project

32

Presentation     

10

Total 

150

Participation requirements

Your participation in lectures, tutorials and online activities will enhance your understanding of the unit content and therefore the quality of your assessment responses. Lack of participation may result in your inability to satisfactorily pass assessment items. So you are urged to fully participate in all the activities in the unit to derive maximum value.

Required IT skills

Specialist IT skills will be required in the unit. Python programming would be the environment used for developing the unit skills and learning outcomes.

This unit involves online meetings in real time using the Virtual Room in your UCLearn 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 UCLearn 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

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