FIT5215 · Monash University
Deep learning
- Credit points
- 6
- Level
- 5
- Workload
- ≈ 12 h / week
- Faculty
- Information Technology
What it covers
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.
Assessment
| Task | Type | Weight |
|---|---|---|
| Scheduled final assessment (2 hours and 10 minutes) | Examination | 40% |
| Assessment 1b | Quiz / Test | 20% |
| Assessment 1a | Artefact | 20% |
| Assessment 2 | Artefact | 20% |
| Assignment 2 | Artefact | 20% |
| In class test 1 | Quiz / Test | 10% |
| In class test 2 | Quiz / Test | 10% |
| Scheduled final assessment (2 hours and 10 minutes) | Examination | 40% |
| Assignment 1 | Artefact | 20% |
When and where
| Teaching period | Campus | Mode |
|---|---|---|
| First semester | Suzhou (SEU) | On-campus |
| Second semester | Malaysia | On-campus |
| Clayton | Flexible |