MTHM610 | 2024/5 | University of Exeter

MODULE TITLE	Environmental Intelligence: Methods and Models	CREDIT VALUE	15
MODULE CODE	MTHM610	MODULE CONVENER	Unknown

DURATION: TERM	1	2	3
DURATION: WEEKS	11	0	0

Number of Students Taking Module (anticipated)	30

DESCRIPTION - summary of the module content

In this module you will learn the fundamentals behind a wide variety of data science, machine learning, artificial intelligence and statistical approaches to detecting and modelling patterns in data. You will learn the differences and similarities between numerical models (e.g. climate and weather models), statistical/stochastic modelling and machine learning approaches. You will develop the skills to decide which methods and techniques are most appropriate to use in different scenarios and how to apply them to real-life environmental datasets. You will learn how to integrate datasets from multiple sources to create new information and data products to help gain insight into important environmental challenges.

Co-requisite modules: MTHM609

AIMS - intentions of the module

The aim of this module is to equip you with the theoretical understanding and practical skills you will need to perform modern techniques in data science, machine learning, artificial intelligence and statistics for detecting and modelling patterns in data as well as understanding and interpreting the outputs. You will study one or more advanced topics in some depth.

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 an understanding of current developments in data science, machine learning, artificial intelligence and statistics.

2. Demonstrate an understanding of the strengths and limitations of different modelling approaches.

3. Demonstrate the ability to apply advanced statistical methodology across a variety of settings.

Discipline Specific Skills and Knowledge

4. Demonstrate an understanding the application of statistics, machine learning and AI in environmental challenges and real-world settings.

5. Demonstrate an understanding of modelling data with dependence.

6. Demonstrate the ability to self-learn further details of the methodology introduced.

Personal and Key Transferable / Employment Skills and Knowledge

7. Perform complex statistical analysis.

8. Use R/RStudio (or other software) to implement methods in data science, machine learning, artificial intelligence and statistics.

9. Use of learning resources effectively.

10. Communicate the results of data analysis clearly and accurately, both in writing and verbally.

SYLLABUS PLAN - summary of the structure and academic content of the module

Whilst the module’s precise content may vary from year to year, an example of an overall structure is as follows:

Statistical modelling (linear modelling, generalised linear models)
Machine learning & AI (decision trees, random forests, support vector machines, neural nets)
Numerical and stochastic models
Data integration
Uncertainty quantification

Other suitable topics may also be offered.

LEARNING AND TEACHING

LEARNING ACTIVITIES AND TEACHING METHODS (given in hours of study time)

Scheduled Learning & Teaching Activities	33	Guided Independent Study	117	Placement / Study Abroad	0

DETAILS OF LEARNING ACTIVITIES AND TEACHING METHODS

Category	Hours of study time	Description
Scheduled Learning and Teaching Activities	22	Lectures
Scheduled Learning and Teaching Activities	11	Hands-on practical sessions
Guided Independent Study	53	Self-study & background reading
Guided Independent Study	64	Assessed data analyses, report writing

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
Feedback on unassessed practical session activities, problem sheets or data analyses	10 x 1 hour	1-10	Oral, in practical sessions

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 – practical modelling exercises and theoretical problems	100	30 hours	1-10	Written and oral

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 – practical modelling exercises and theoretical problems	Coursework – practical modelling exercises and theoretical problems (100%)	1-10	Ref/Def period

RE-ASSESSMENT NOTES

Please refer to the TQA section on Referral/Deferral: https://as.exeter.ac.uk/academic-policy-standards/tqa-manual/aph/consequenceoffailure/

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
Set	Faraway, Julian J.	Extending the Linear Model with R: Generalized Linear, Mixed Effects and Nonparametric Regression Models		Chapman and Hall/CRC	2016
Set	Wakefield, Jon	Bayesian and Frequentist Regression Methods	Vol. 23	Springer	2013
Set	Heumann, C., Schomaker, M., Shalabh	Introduction to Statistics and Data Analysis: With Exercises, Solutions and Applications in R	1st	Springer	2016	978-3319834566
Set	Lantz, B.	Machine Learning with R: Expert Techniques for Predictive Modeling	3rd	Packt	2019	978-1788295864
Set	James, G., Witten, D., Hastie, T., Tibshirani, R.	An Introduction to Statistical Learning: with Applications in R		Springer	2013	978-1461471370

CREDIT VALUE	15	ECTS VALUE	7.5

PRE-REQUISITE MODULES	None
CO-REQUISITE MODULES	None

NQF LEVEL (FHEQ)	7	AVAILABLE AS DISTANCE LEARNING	No
ORIGIN DATE	Wednesday 12th January 2022	LAST REVISION DATE	Tuesday 17th January 2023

KEY WORDS SEARCH	Statistical modelling; machine learning; data science; artificial intelligence; methods

Environmental Intelligence: Methods and Models - 2024 entry