FIT5215 · Deep learning

Overview

Modern machine learning provides core underlying theory and techniques to data science and artificial intelligence. This unit is for you 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. Learning activities will focus on designing machine learning systems, a broad landscape of supervised and unsupervised learning methods with a focus on modern deep learning knowledge for data analytics including deep neural networks, representation learning and embedding methods, and deep models used for time-series data which are rapidly used in science and industry.

Offerings

Assessment

The Handbook lists an examination for this unit.

Assessment details may change. Please refer to the assessment information in Moodle closer to the start of the teaching period.

Requisites

Learning outcomes

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.

Workload

Minimum total expected workload to achieve the learning outcomes for this unit is 144 hours per semester typically comprising a mixture of scheduled online and face to face learning activities and independent study. Independent study may include associated reading and preparation for scheduled teaching activities.

Student experience, not official rules. Nothing here changes what the Handbook says.