Full Title  Applied Statistics and Probability 

Short Title  Applied Statistics and Probabi 









Description 

This module covers the statistics and probability required for a Masters in Engineering. The learner will gain the expertise to interpret the probabilistic models used in the engineering literature. It will cover statistical methods to analyse and quantify processes. It will enable learners to model problems using probabilistic and statistical mathematical methods. 
Indicative Syllabus 

Probability Theory
Estimation Theory
MMSE, MAP, BLUE estimators Bias/Variance tradeoff

Learning Outcomes 

On completion of this module the learner will/should be able to 

Assessment Strategies 

A terminal exam and continuous assessment will be used to assess the module. To reinforce the theoretical principles covered in lectures, learners will participate in project work. The learner will complete a final exam at the end of the semester. The learner is required to pass both the continuous assessment and terminal examination element of this module. 
Module Dependencies 

Pre Requisite Modules 
Co Requisite Modules 
Incompatible Modules 
Coursework Assessment Breakdown  % 

Course Work / Continuous Assessment  60 % 
End of Semester / Year Formal Examination  40 % 
Coursework Assessment Breakdown 

Description  Outcome Assessed  % of Total  Assessment Week 

CA 1  1,2,3,4  30  Week 6 
Project  1,2,3,4,5,6,7  30  Week 12 
End Exam Assessment Breakdown 

Description  Outcome Assessed  % of Total  Assessment Week 

Final Exam  1,2,3,4,5,6,7  40  End of Semester 
Mode Workload 

Type  Location  Description  Hours  Frequency  Avg Weekly Workload 

Lecture  Lecture Theatre  Lecture  2  Weekly  2.00 
Laboratory Practical  Computer Laboratory  Laboratory Practical  2  Fortnightly  1.00 
Independent Learning  Not Specified  Independent Learning  7  Weekly  7.00 
Total Average Weekly Learner Workload 3.00 Hours 

Mode Workload 

Type  Location  Description  Hours  Frequency  Avg Weekly Workload 

Total Average Weekly Learner Workload 0.00 Hours 

Mode Workload 

Type  Location  Description  Hours  Frequency  Avg Weekly Workload 

Total Average Weekly Learner Workload 0.00 Hours 

Mode Workload 

Type  Location  Description  Hours  Frequency  Avg Weekly Workload 

Lecture  Online  Theory Lecture  1  Weekly  1.00 
Independent Learning  Not Specified  Independent Learning  8.5  Weekly  8.50 
Laboratory Practical  Online  Laboratory Practical  0.5  Weekly  0.50 
Total Average Weekly Learner Workload 1.50 Hours 

Resources 

Book Resources 

Other Resources 
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Url Resources 
x 
Additional Info 
ISBN BookList 

Book Cover  Book Details 
JosÃ© Unpingco 2016 Python for Probability, Statistics, and Machine Learning Springer ISBN10 3319307150 ISBN13 9783319307152 

Christopher M. Bishop 2007 Pattern Recognition and Machine Learning (Information Science and Statistics) Springer ISBN10 0387310738 ISBN13 9780387310732 