CO902 Learning Outcomes
LEARNING OUTCOMES (By the end of the module the student should be able to....) |
Teaching and learning methods enabling students to achieve this learning outcome. |
Assessment methods will measuring the achievement of this learning outcome. |
(a) Subject knowledge and understanding The student should have a systematic appreciation of principles of Statistical Inference. They should have a conceptual understanding of modern machine learning. They should be implement both critically and practically a range of approaches for the analysis of noisy data. |
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Oral Presentation |
(b) Key Skills
Work effectively in a team. Create statistical models of uncertain systems.
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Lectures, reading, classwork |
Reports; oral examination. |
(c) Cognitive Skills They should be able to “think statistically” with initiative about analyzing noisy data |
Classwork |
Written reports, oral examination. |
(d) Subject-Specific/Professional Skills They should be experienced in developing and implementing statistical methods with unpredictable real-world data |
Classwork |
Written reports |