FIT5219 Advanced learning and cognitive systems
Faculty of Information Technology
FIT5219 Advanced learning and cognitive systems is a level 5, 6-credit-point, postgraduate unit from the Faculty of Information Technology. It isn't offered in 2020. It needs FIT5201.
- Credit points
- 6
- Offered in 2020
- Not offered
- Workload
- 12 hours
- per semester
This is the 2020 handbook entry. See the 2022 entry.
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Requisites
Before FIT5219
Prerequisites
Pass these before you enrol.
After FIT5219
No unit lists FIT5219 as a prerequisite in the 2020 handbook.
Enrolment rules
Prerequisite - Students are expected to have the following background:
- Knowledge of basic computer science principles and skills, at a level sufficient to write a reasonably non-trivial computer program in python
- Familiarity with the probability theory
- Familiarity with linear algebra
- Familiarity with deep learning TensorFlow platform
Overview
Deep learning (DL) is one of the most highly sought after skills in AI. It is one of the most important breakthroughs in technology and has become a driving force for AI research and applications. This course will focus on advanced learning and cognitive systems which uses modern knowledge in deep learning, machine learning, computer vision and language technology to build AI applications. It covers the foundation as well as advanced knowledge in deep learning and related disciplines. Deep neural networks, Convolutional networks, RNNs, LSTM, GRU and optimisation techniques such as Adam, Dropout, BatchNorm will be covered. It then focuses on modern techniques including deep reinforcement learning, deep generative models and how they might be applied in computer vision and language technology tasks. Learning activities include designing deep neural networks and CNN-based systems for image/video classification, representation learning on different types of structured, unstructured and semi-structured data, deep generative models, cognitive vision/language systems and deep reinforcement learning.
Offerings in 2020
The 2020 handbook lists no offerings for FIT5219.
Learning outcomes
When you finish this unit, you should be able to:
- 1
Analyse problems and big datasets with a range of deep learning tools
- 2
Design solutions to a real world problem using advanced learning systems, what is involved in designing such systems and strategy to maintain them
- 3
Describe and apply a range of advanced tools in cognitive and learning systems such as DNN, CNN RNN, LSTM and deep reinforcement learning to selected advanced AI-based systems such as vision and NLP
- 4
Develop advanced unsupervised feature learning models and representation learning models
- 5
Communicate the results of an analysis, experiments and learning systems for both specific and broad audiences
Workload and teaching
Minimum total expected workload equals 12 hours per week comprising:
- Two hours/week lectures
- Two hours/week laboratories
A minimum of 8 hours per week of personal study for completing lab/tutorial activities, assignments, private study and revision.
Where it fits
FIT5219 is part of 1 area of study in the 2020 handbook.
Common questions
What are the prerequisites for FIT5219?
You need FIT5201 before you enrol. Enrolment rules also apply.
When is FIT5219 offered?
FIT5219 has no offerings listed in the 2020 handbook.
How much work is FIT5219?
The handbook expects about 12 hours of study across the semester. No students have rated its difficulty yet.
Which majors and minors include FIT5219?
FIT5219 is part of Software engineering.