Overview
Modern machine learning provides core underlying theory and techniques to data science and artificial intelligence. This unit is for students to develop practical knowledge of modern machine learning and deep learning and how they can be used in real-world settings such as image recognition or text clustering via neural embeddings. … For more content click the Read More button below.
Offerings
S2-01-CLAYTON-ON-CAMPUS
T3-57-OS-CHI-SEU-ON-CAMPUS
Requisites
Prerequisite
Contacts
Chief Examiner(s)
Professor Dinh Phung
Notes
IMPORTANT NOTICE:
Scheduled teaching activities and/or workload information are subject to change in response to COVID-19, please check your Unit timetable and Unit Moodle site for more details.
Learning outcomes
On successful completion of this unit, you should be able to:
1.
Describe the life cycle of a machine leaning system, what is involved in designing such systems and strategy to maintain them;
2.
Describe what deep learning (DL) is, access what makes DL work or fail and where they should be applied;
3.
Develop and apply deep neural networks, convolutional neural networks, recurrent neural networks and different optimisation strategies for training them;
4.
Develop unsupervised feature learning models and representation learning models.