Pattern Recognition and Machine Learning (11482.1)
|Level:||Level 3 - Undergraduate Advanced Unit|
|Faculty:||Faculty of Science and Technology|
|Discipline:||Academic Program Area - Technology|
UC - Canberra, Bruce
Year Teaching Period Convener Mode of Delivery 2020 Semester 2 PROF Dharmendra SHARMA (Ph: +61 2 62012131 ) ON-CAMPUS
Possible changes to your unit's learning activities and assessment items
As a result of the Australian Government's directives requiring physical distancing and restrictions on movement because of the COVID-19 pandemic, you may find that learning activities and/or assessment items in some units you are studying have changed. These changes will not be updated in the published Unit Outline but will be communicated to you via your unit’s UCLearn(Canvas) teaching site. The new learning activities and/or assessment items will continue to meet the unit's learning outcomes, as described in the Unit Outline.
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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.
After successful completion of this unit, students will be able to:
1. Understand, describe and critique pattern recognition, machine learning and deep learning techniques;
2. Identify and select suitable modelling, learning and prediction techniques to solve a problem;
3. Design and implement a machine learning solution; and
4. Appraise ethical and privacy issues of artificial intelligence techniques.
Four hours of problem-based learning activities/interactive workshops/practical work in laboratory classes on-campus per week.
Must have passed 48 credit points.
Working knowledge of programming (e.g. scripting languages), discrete mathematics, algebra and numerical analysis.
11513 Pattern Recognition and Machine Learning PG