Module 30193 (2019)
Module description - Mathematical Modelling and Decision Making
The Module Description is a strict subset of the Syllabus Page.
|Module Title||Mathematical Modelling and Decision Making|
|School||School of Computer Science|
|Member of Staff||Peter Tino Martin Russell|
|Semester||Semester 2 - 20 credits|
Provided via lectures and guided independent study.
Contact hours: Total: 200 hours, Lecture: 33 hours, Guided independent study: 167 hours.
Artificial and machine intelligence are increasingly reliant on models of the world that account for uncertainty - probabilistic models. When a robot moves around it is constantly trying to make inferences about the world based on prior beliefs, but also new data. The new data are unreliable, and the prior beliefs may be only approximate or weighted beliefs; the robot needs to be able to account for all the information it has to estimate the probability of success if it takes a certain course of action. Similarly, in medicine, we are constantly faced with problems of disease diagnosis (or trying to establish the cause of a disease), and we only have probabilistic information about the most likely disease (or the most likely cause). This module will look at all the tools and principles behind progress in these and other challenging problems.
On successful completion of this module, the student should be able to:
Assessments: 2hr Examination (80%), Continuous Assessment (20%) Reassessment: 2hr Examination (100%)