Computer Science

ECMM459 - Statistical Modelling (2023)

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MODULE TITLEStatistical Modelling CREDIT VALUE15
MODULE CODEECMM459 MODULE CONVENERDr Tinkle Chugh (Coordinator)
DURATION: TERM 1 2 3
DURATION: WEEKS 7
Number of Students Taking Module (anticipated) 90
DESCRIPTION - summary of the module content

*** This module is a “professional” module intended to be taught in a short-fat format based around 3-day teaching blocks, as part of the MSc Data Science (Professional) programme. ***

In this course we look at the concepts and methods of modern statistics in greater detail. The course will cover various topics in statistical modelling with Bayesian flavor, including generalised linear models, Hierarchical statistical models, Generative and Discriminative models, Hidden Markov models, use of Markov Chain Monte Carlo and Gaussian processes. The module will include practical application of these techniques as well as theoretical underpinnings and model choice.

Pre-requisites: ECMM456 Fundamentals of Data Science (Professional)

Co-requisites: None.

AIMS - intentions of the module

The aim of this module is to introduce you to modern methods in statistics, both conceptually and computationally.

INTENDED LEARNING OUTCOMES (ILOs) (see assessment section below for how ILOs will be assessed)
On successful completion of this module you should be able to:
 
Module Specific Skills and Knowledge
 
1. Demonstrate a sound understanding of the reasoning behind choice of methods in statistical modelling.
 
2. Apply a range of statistical modelling techniques to real-life situations and datasets.
 
3.Perform data analyses by understanding the underlying principles behind different methods.
 
Discipline Specific Skills and Knowledge
 
4. Show sufficient knowledge of modern statistical methods both conceptual and computational.
 
Personal and Key Transferable / Employment Skills and Knowledge
 
5.  Reason using abstract ideas, formulate and solve problems and communicate reasoning and solutions effectively in writing.
 
6. Use learning resources appropriately.
 
7. Exhibit self management and time management skills.
 
SYLLABUS PLAN - summary of the structure and academic content of the module
Topics will include:
 
• Basics of Bayesian statistical modelling
 
• Generalised Linear Models
 
• Markov Chain Monte Carlo
 
• Generative and discriminative models
 
• Hierarchical statistical modelling
 
• Hidden Markov models
 
• Introduction to Gaussian Processes
 

 

LEARNING AND TEACHING
LEARNING ACTIVITIES AND TEACHING METHODS (given in hours of study time)
Scheduled Learning & Teaching Activities 34.00 Guided Independent Study 46.00 Placement / Study Abroad 0.00
DETAILS OF LEARNING ACTIVITIES AND TEACHING METHODS

Category

Hours of study time

Description

Scheduled learning and teaching activities

20

Lectures

Scheduled learning and teaching activities

14

Workshop/Practical classes in a computer lab

Guided independent study

46

 Coursework preparation and self-study

 

    

 

ASSESSMENT
FORMATIVE ASSESSMENT - for feedback and development purposes; does not count towards module grade

Form of Assessment

Size of Assessment (e.g. duration/length)

ILOs Assessed

Feedback Method

Exercise/Quiz

1h x 4

All

Written

 

SUMMATIVE ASSESSMENT (% of credit)
Coursework 100 Written Exams 0 Practical Exams 0
DETAILS OF SUMMATIVE ASSESSMENT

Form of Assessment

% of Credit

Size of Assessment (e.g. duration/length)

ILOs Assessed

Feedback Method

Coursework report

100

2000-3000 words

All

Written
         

 

DETAILS OF RE-ASSESSMENT (where required by referral or deferral)

Original Form of Assessment

Form of Re-assessment

ILOs Re-assessed

Time Scale for Re-assessment

Coursework report

Coursework report

All

 Ref/Def period

 

RE-ASSESSMENT NOTES

Reassessment will be by coursework  in the failed or deferred element only. For referred candidates, the mark will be capped at 50%. For deferred candidates, the mark will be uncapped.

RESOURCES
INDICATIVE LEARNING RESOURCES - The following list is offered as an indication of the type & level of
information that you are expected to consult. Further guidance will be provided by the Module Convener

Reading list for this module:

Type Author Title Edition Publisher Year ISBN Search
Set Gelman, A., Carlin, J., Stern, H., Dunson, D., Vehtari, A. and Rubin, D. Bayesian data analysis 3rd CRC 2008 [Library]
Set Gamerman, D. and Lopes H. F. Markov Chain Monte Carlo: Stochastic Simulation for Bayesian Inference CRC Press 2006 [Library]
Set Banerjee, S., Bradley, P. Carlin, A.& Gelfand, E. Hierarchical Modeling and Analysis for Spatial Data CRC Press 2014 [Library]
Set Donovan, Therese and Mickey, Ruth M. Bayesian Statistics for Beginners: a step-by-step approach OUP Oxford 2019 9780198841296 [Library]
Set Carl Edward Rasmussen, Christopher K. I. Williams Gaussian Processes for Machine Learning MIT Press 2006 978-0262182539 [Library]
Set Murphy, K. Machine Learning: A Probabilistic Perspective 1st MIT Press 2012 978-0-262-018029 [Library]
CREDIT VALUE 15 ECTS VALUE 7.5
PRE-REQUISITE MODULES ECMM456
CO-REQUISITE MODULES
NQF LEVEL (FHEQ) 7 AVAILABLE AS DISTANCE LEARNING No
ORIGIN DATE Monday 05 August 2019 LAST REVISION DATE Wednesday 18 January 2023
KEY WORDS SEARCH Statistical Modelling