Key facts
UNE unit code: COSC551
*You are viewing the 2026 version of this unit which may be subject to change in future.
- Trimester 1 - On Campus
- Trimester 1 - Online
- Armidale Campus
- Yes
- No
- Yes
- 6
Unit information
Deep learning is one of the most important techniques in Artificial Intelligence, underpinning rapidly advancing innovative technologies such as autonomous systems, biometrics, cybersecurity and digital assistance. This unit introduces you to deep learning using a range of toolkits and technologies commonly applied within industry and research settings. You will gain invaluable hands-on experience building deep learning workflows to solve computer vision and natural language processing problems using advanced techniques. Topics covered include computer vision, natural language processing and generative AI, using Deep Convolutional Neural Networks (DCNNs), Transformers, Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs) and Generative Pre-trained Transformers (GPT). You will understand the theoretical concepts underpinning deep learning best practices, with a strong focus on applied skills. The unit culminates in self-directed deep learning project applying knowledge and skills learned.
Offerings
For further information about UNE's teaching periods, please go to Principal Dates.
| Teaching period | Mode/location |
|---|---|
| Trimester 1 | On Campus, Armidale Campus |
| Trimester 1 | Online |
*Offering is subject to availability
Intensive schools
There are no intensive schools required for this unit.
Enrolment rules
Notes
Please refer to the student handbook for current details on this unit.
Unit coordinator(s)
Learning outcomes
Upon completion of this unit, students will be able to:
- explain the fundamentals of deep learning including tensors and their operations, gradient descent and backpropagation;
- solve complex problems using a range of deep learning toolkits and technologies commonly applied within industry and research settings;
- analyse and interpret advanced deep learning principles and apply deep convolutional neural networks to computer vision tasks;
- apply advanced principles of deep learning using transformers for natural language processing;
- design and implement an effective deep learning workflow to solve problems using advanced deep learning techniques, applying best practices; and
- demonstrate effective oral communication skills to justify decisions and approach to solving a complex deep learning problem.
Assessment information
Assessments are subject to change up to 8 weeks prior to the start of the teaching period in which you are undertaking the unit.
| Title | Must Complete | Weight | Offerings | Assessment Notes |
|---|---|---|---|---|
| Final Exam (Supervised) - Assurance Task | Yes | 30% | All offerings | Final Exam: Supervised MyLearn Quiz. Students must pass the Final Examination and also pass the sum of the Final Examination and Assessment 5 in aggregate in order to pass the unit. |
| Formative Assessment 1: Fundamentals of Deep Learning and Computer Vision | Yes | 10% | All offerings | Students complete an open-book timed quiz covering the fundamentals of Deep Learning and Computer Vision. |
| Formative Assessment 2: Major Project Plan | Yes | 10% | All offerings | Students submit a scaffolded plan for their major project. |
| Formative Assessment 3: Deep Learning for Time-series | Yes | 10% | All offerings | Students complete an open-book timed quiz covering advanced Computer Vision methods and Time-series. |
| Formative Assessment 4: Deep Learning for Natural Language Processing | Yes | 10% | All offerings | Students complete an open-book timed quiz covering Natural Language Processing. |
| Student Research Portfolio - Assurance Task | Yes | 30% | All offerings | Students design and implement a custom workflow to solve a Computer Vision OR Time-series OR Natural Language Processing problem. Students submit a git project and video presentation of their workflow. Students must pass the Final Examination and also pass the sum of the Final Examination and Assessment 5 in aggregate in order to pass the unit. |
Learning resources
Textbooks are subject to change up to 8 weeks prior to the start of the teaching period in which you are undertaking the unit.
Note: Students are expected to purchase prescribed material. Please note that textbook requirements may vary from one teaching period to the next.
Deep Learning with Python
ISBN: 9781633436589
Chollet, F. and Watson, M., Manning 3rd ed. 2025
Text refers to: All offerings
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