CM50265: Machine learning 2
[Page last updated: 23 October 2023]
| Academic Year: | 2023/24 |
| Owning Department/School: | Department of Computer Science |
| Credits: | 6 [equivalent to 12 CATS credits] |
| Notional Study Hours: | 120 |
| Level: | Masters UG & PG (FHEQ level 7) |
| Period: |
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| Assessment Summary: | CW 40%, EX 60% |
| Assessment Detail: |
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| Supplementary Assessment: |
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| Requisites: | |
| Learning Outcomes: |
At the end of this unit, students will be able to:
* Demonstrate a systematic knowledge of state-of-the-art ML approaches and an awareness of the latest ongoing research in the field * Develop and evaluate critically advanced ML models for real-world problems * Identify and implement appropriate and original algorithms to perform inference * Make predictions from models and account for uncertainty |
| Aims: | This unit covers the breadth of machine learning topics as well as providing detailed treatment of advanced methods that are representative of the different categories of ML approaches. |
| Skills: | Intellectual skills:
* Demonstrate an advanced conceptual understanding of ML modelling (T, F, A) * Critical analysis of advanced models and algorithms (T, F, A) Practical skills: * Produce practical implementations of advanced ML algorithms (T, F, A) * Evaluate and critique algorithms on complex data (T, F, A) Transferable skills: * Numerical programming and independent learning (F, A) * Technical report writing and presentation skills (F, A) |
| Content: | Topics covered will normally include a range of subjects: ensemble learning, Natural Language Processing (NLP), and various deep learning models such as Multi-Layer Perceptron (MLP), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Generative Adversarial Networks (GAN), attention mechanisms and transformers. |
| Course availability: |
CM50265 is Compulsory on the following courses:Department of Computer Science
CM50265 is Optional on the following courses:Department of Computer Science
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Notes:
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