CM22016: Foundations and frontiers of machine learning
[Page last updated: 09 August 2024]
| Academic Year: | 2024/25 |
| Owning Department/School: | Department of Computer Science |
| Credits: | 10 [equivalent to 20 CATS credits] |
| Notional Study Hours: | 200 |
| Level: | Intermediate (FHEQ level 5) |
| Period: |
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| Assessment Summary: | CWES 50%, EXCB 50% |
| Assessment Detail: |
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| Supplementary Assessment: |
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| Requisites: | BEFORE TAKING THIS MODULE YOU MUST ( take CM12001 AND take CM22009 ) |
| Learning Outcomes: |
On completion of the unit, the students will be able to:
1. demonstrate a basic understanding of the important theoretical concepts and algorithms in modern machine learning,
2. demonstrate familiarity with state-of-the-art applications of machine learning and open research questions,
3. appraise the suitability of various machine learning methods for a given application. |
| Synopsis: | You will explore the foundational theory and practice of machine learning. You will practise the mathematical, statistical, and computational skills used in modern machine learning methods, appraise the suitability of various methods for a given application, and consider state-of-the-art developments. |
| Content: | Topics covered by this unit will typically include: theory of machine learning, optimization, designing custom machine learning algorithms, state-of-the-art applications of machine learning, and current research in machine learning.
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| Course availability: |
CM22016 is Compulsory on the following courses:Department of Computer Science
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Notes:
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