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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.



Lectures; Classwork including in groups/teams, plus feedback; problem solving and feedback; directed reading; private study.


Oral examination; Written reports;

Oral Presentation

(b) Key Skills Work effectively in a team. Create statistical models of uncertain systems.

 


Classwork

Lectures, reading, classwork


Oral Presentations;

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