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OTHM Level 7 Deep Learning (T/651/3613) Assignment Brief 2026
| University | OTHM Qualifications |
| Subject | Deep Learning (T/651/3613) |
Deep Learning Assignment Brief
| Qualification | OTHM Level 7 Diploma in Artificial Intelligence (610/4802/1) |
| Unit Reference Code | T/651/3613 |
| Unit Name | Deep Learning |
| Credit | 20 |
| GLH | 100 |
| TQT | 200 |
| Mandatory / Optional | Mandatory |
| Unit Grading Type | Pass / Fail |
Assignment Aim
This unit aims to provide learners with a deep understanding of the fundamental concepts and advanced methodologies in deep learning. Learners will explore the theoretical underpinnings of modern deep learning techniques and their practical applications across various domains such as computer vision, natural language processing, and graph-based tasks. The module will enable learners to evaluate different deep learning approaches, understand their limitations, and apply these techniques to solve real-world problems. By the end of the unit, learners will have the knowledge and skills necessary to engage in deep learning research and practice.
Learning Outcomes and Assessment Criteria
| Learning Outcome – The learner will: | Assessment Criteria – The learner can: |
| 1. Understand the underlying theoretical concepts of modern deep learning methods. | 1.1 Describe the fundamental principles of deep learning.
1.2 Explain the differences between supervised and unsupervised learning. 1.3 Critically analyse the role of gradient-based optimization in deep learning. 1.4 Evaluate the impact of network depth on model performance. 1.5 Discuss the theory of Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and introduce Diffusion Models. |
| 2. Be able to compare, characterise and quantitatively evaluate various deep learning approaches. | 2.1 Describe and compare different architectures used in deep learning.
2.2 Critique the performance of CNNs in image analysis tasks. 2.3 Analyse the effectiveness of RNNs for sequential data processing. 2.4 Critically evaluate the performance of generative models in various tasks, including GANs and Diffusion Models. 2.5 Discuss transformer-based approaches to Large Language Models (LLMs). |
| 3. Understand the limitations of deep learning. | 3.1 Identify common challenges faced in deep learning.
3.2 Explain the concept of overfitting and strategies to mitigate it. 3.3 Analyse the ethical considerations associated with deep learning. 3.4 Critically evaluate the robustness and generalisation ability of deep learning models. |
| 4. Be able to apply deep learning techniques to real-world problems in computer vision, speech, text analysis, and graph processing. | 4.1 Implement CNNs for image classification tasks.
4.2 Apply RNNs to speech recognition and machine translation tasks. 4.3 Develop generative models for synthetic data generation. 4.4 Implement graph neural networks (GNNs) as a generalisation of CNN theory for graph-based data analysis. |
Assessment
To achieve a ‘pass’ for this unit, learners must provide evidence to demonstrate that they have fulfilled all the learning outcomes and meet the standards specified by all assessment criteria.
| Learning Outcomes to be met | Assessment Criteria to be covered | Assessment type | Word count (approx. length) |
| LO1 – LO4 | All AC’s under LO1 – LO4 | Coursework | 4,000 words |
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