Last modified: 05 Aug 2021 13:04
This course will allow students to use cutting-edge AI technologies to investigate the creation and application of AI systems. Such tools include deep learning libraries and simulation environments.
Study Type | Postgraduate | Level | 5 |
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Term | First Term | Credit Points | 15 credits (7.5 ECTS credits) |
Campus | Aberdeen | Sustained Study | No |
Co-ordinators |
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On this module students will apply the theoretical and practical knowledge they have gained on previous modules to undertake several AI mini-projects. Each project will exercise a different AI application library (e.g., TensorFlow, PyTorch or Keras), and students will therefore understand how AI techniques e.g., classifier and neural network systems, reinforcement learning systems and simulation/evaluation systems, can be combined to create an end-to-end deployable AI solution.
Information on contact teaching time is available from the course guide.
Assessment Type | Summative | Weighting | 100 | |
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Assessment Weeks | Feedback Weeks | |||
Feedback |
Knowledge Level | Thinking Skill | Outcome |
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There are no assessments for this course.
Assessment Type | Summative | Weighting | 100 | |
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Assessment Weeks | Feedback Weeks | |||
Feedback |
Knowledge Level | Thinking Skill | Outcome |
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Knowledge Level | Thinking Skill | Outcome |
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Conceptual | Understand | Students will understand how libraries for AI system creation are applied and used in practice. |
Conceptual | Apply | Students will be able to combine different components into an end-to-end solution. |
Conceptual | Evaluate | Students will be able to validate the effectiveness of their solution. |
Conceptual | Apply | Students will be able to document – in a reproducible manner – their approach to AI system construction. |
Reflection | Evaluate | Students will be able to reflect and modify their design in response to validation of an AI system’s performance. |
Reflection | Analyse | Students will reflect on their new knowledge and apply it to a practical example. |
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