DAI405 Introduction to Deep Learning (K/651/0603) Assignment Brief 2026
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DAI405 Introduction to Deep Learning (K/651/0603) Assignment Brief 2026
DAI405 Assignment Brief
| Qualification | Level 4 Diploma in Artificial Intelligence (610/3934/2) |
|---|---|
| Unit Code | DAI405 |
| Unit Title | Introduction to Deep Learning |
| Unit Reference | K/651/0603 |
| Credits | 20 |
| TQT | 200 |
| GLH | 120 |
Assignment Aim
In this unit learners will develop an understanding of the principles and applications of deep learning. They will revisit concepts associated with data science and machine learning before exploring key concepts relating to neural networks. Students will have the opportunity to practice solving real-life business problems using various neural network models and, subsequently, to analyse the results from such models. Students will use their knowledge and skills to evaluate the results of an image classification model and suggest how deep learning models can be improved.
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 basic machine learning concepts that apply when developing deep learning models.
|
1.1 Explain key concepts in machine learning |
| 1.2 Explain the process of data preparation / cleansing. | |
| 1.3 Discuss the functions of classifier and ensemble in Machine Learning. | |
| 2. Understand how to develop basic deep learning models. | 2.1 Explain key concepts of deep learning models |
| 2.2 Assess the use of these key concepts in deep learning modelling. | |
| 3. Be able to apply Neural network models to carry out simple tasks using popular frameworks. | 3.1 Describe the purpose and deliverable outcome of Neural network models used in deep learning |
| 3.2 Select an appropriate Neural network model for an image classification. | |
| 3.3 Model a selected Neural network model onto a given dataset. | |
| 4. Be able to interpret results using prepared sample data to preprocess deep learning models.
|
4.1 Select appropriate metric to evaluate the results of an image classification model |
| 4.2 Analyse the results produced and summarize how the deep learning model can be further improved |
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