ETX3250 · Monash University
Predictive analytics and machine learning
- Credit points
- 6
- Level
- 3
- Workload
- ≈ 12 h / week
- Faculty
- Business and Economics
What it covers
Many problems in business including sales and inventory forecasting, credit scoring, recommender systems in online commerce and fraud detection use advanced tools for data analytics. This unit covers some of the most popular tools that may include tree-based methods, boosting, bagging, support vector machines, neural networks and deep learning. The algorithmic details of each method, their implementation using popular software tools (such as R) and their application to real business problems will all be covered.
Assessment
| Task | Type | Weight |
|---|---|---|
| 1 - Written | Written | 25% |
| 2 - Project | Project | 25% |
| 3 - Examination | Examination | 50% |
When and where
| Teaching period | Campus | Mode |
|---|---|---|
| First semester | Caulfield | Blended |