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Module Detailed Information for [CS5340]
Academic Year : 2018/2019 Semester : 2
Correct as at 18 Feb 2019 05:00

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Module Information
Module Code :
Module Title : Uncertainty Modelling in AI
Module Description : The module covers modelling methods that are suitable for reasoning with uncertainty. The main focus will be on probabilistic models including Bayesian networks and Markov networks. Topics include representing conditional independence, building graphical models, inference using graphical models and learning from data. Selected applications in various domains such as speech, vision, natural language processing, medical informatics, bioinformatics, data mining and others will be discussed.
Module Examinable : -
Exam Date : 27-04-2019 PM
Modular Credits : 4
Pre-requisite : (ST1232 Statistics for Life Sciences or ST2131 Probability or ST2334 Probability and Statistics) and CS3243 Introduction to Artificial Intelligence
Preclusion : Nil
Module Workload (A-B-C-D-E)* : 3-0-0-3-4
Remarks : Nil
* A: no. of lecture hours per week
B: no. of tutorial hours per week
C: no. of laboratory hours per week
D: no. of hours for projects, assignments, fieldwork etc per week
E: no. of hours for preparatory work by a student per week

Lecture Time Table
Class TypeWeek TypeWeek DayStartEndRoom

Tutorial Time Table
Attention: The tutorial timetables could be updated from time to time. Students are advised to check regularly for the latest update on the change of tutorial timing.
Class TypeWeek TypeWeek DayStartEndRoom Iteration
Available in Tutorial Balloting [Iteration 2].

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