DAI502 Reinforce Machine Learning (R/651/0606) Assignment Brief 2026
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DAI502 Reinforce Machine Learning (R/651/0606) Assignment Brief 2026
DAI502 Assignment Brief
| Qualification | Level 5 Diploma in Artificial Intelligence (610/3935/4) |
|---|---|
| Unit Code | DAI502 |
| Unit Title | Reinforce Machine Learning |
| Unit Reference | R/651/0606 |
| Credits | 20 |
| TQT | 200 |
| GLH | 120 |
Assignment Aim
This unit provides students with fundamental understanding of the principles, basic algorithms and practical usage of reinforced machine learning. Students will be introduced to various reinforcement machine learning algorithms, libraries and frameworks typically used to solve business problems. Students will work a with variety of datasets to produce simple designs and implement and evaluate reinforcement learning algorithms.
Learning Outcomes and Assignment Criteria
| Learning Outcomes
When awarded credit for this unit, a learner will: |
Assessment Criteria
Assessment of this learning outcome will require a learner to demonstrate that they can: |
| 1. Understand key concepts in Reinforced machine Learning. | 1.1 Define key concepts in reinforcement machine learning. |
| 1.2 Compare supervised, unsupervised and reinforcement machine learning and their uses. | |
| 1.3 Compare the functional capabilities of different reinforcement machine learning algorithms | |
| 2. Be able to apply reinforcement machine learning algorithms, libraries, and frameworks. | 2.1 Carry out reinforcement machine learning tasks on a given real-life business problem. |
| 2.2 Review models and architectures used to solve real-life reinforcement machine learning problems | |
| 2.3 Apply reinforcement machine learning algorithms, libraries and frameworks to solve practical business problems | |
| 3. Be able to analyse results of reinforcement machine learning.
|
3.1 Present insights of given model on real life business reinforcement learning problems |
| 3.2 Use appropriate evaluation metrics in reinforcement machine learning to analyse performance effectiveness for the given reallife problems | |
| 3.3 Use hyperparameter tuning and optimization to improve performance of reinforcement machine learning models. |
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