Pattern Recognition and Machine Learning PG (11512.1)
|Faculty:||Faculty of Science and Technology|
|Discipline:||Academic Program Area - Technology|
UC - Canberra, Bruce
Year Teaching Period Convener Mode of Delivery 2020 Semester 2 DR Roland GOECKE (Ph: +61 2 62012114 ) ON-CAMPUS
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.
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.
Four hours of problem-based learning activities, interactive workshops and practical work in laboratory classes on campus per week.
Working knowledge of programming (e.g. scripting languages), discrete mathematics, algebra and numerical analysis.
11482 Pattern Recognition and Machine Learning