MTHM602 - Trends in Data Science and AI (2021)

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MODULE TITLETrends in Data Science and AI CREDIT VALUE15
MODULE CODEMTHM602 MODULE CONVENERDr Saptarshi Das (Coordinator)
DURATION: TERM 1 2 3
DURATION: WEEKS 11
Number of Students Taking Module (anticipated) 20
DESCRIPTION - summary of the module content

Recent advances in science and computing technology have resulted in an explosion of available data, in fields as diverse as biology, climate, engineering, finance and medicine. This has led to the development of new data analytic methodologies, aimed at meeting the challenges associated with processing and understanding “Big Data”. In this problem-solving oriented module, you will keep abreast of state-of-the-art data science tools and techniques. Through practical hands-on learning and working with open sourced and in-situ data and sophisticated scientific computing software, you will develop skills and techniques needed to convert complex data sets into useful information. To foster understanding, effective communication is key. Therefore, students will engage with contemporary data science literature, and present their findings in reports, posters, and through digital media to engage with the lay public and with specialists in their chosen science and technology area.

AIMS - intentions of the module

The aim of this module to follow the current trends of modern data science. You will investigate advanced topics in Bayesian Inference, Deep Learning, and Reinforcement Learning, with a focus on relevant applications and datasets in Conservation, Ecology, Environmental and Sustainability Science, Evolution, Human Health, and/or Renewable Energy.

The module will be delivered by engaging with contemporary literature, structured in two-week cycles, following the dynamics of a postgraduate reading group. A reading list will be made available by the module tutors at the start of a cycle. You will either re-create outputs from a research paper/open source collaborative software development program on GitHub, produce a critical appraisal of the published methods and results, or apply the data and/or methodologies from the research paper(s) to a novel problem.

A key learning outcome of the module is the development of effective communication skills. You will work in groups to synthesise key aspects of considered approaches and their application to relevant data sets. Groups will practice presenting their findings at the end of each two-week cycle and prepare for an assessed group presentation in the second half of term.

INTENDED LEARNING OUTCOMES (ILOs) (see assessment section below for how ILOs will be assessed)

Module Specific Skills and Knowledge:

1

Read and discuss current (less than one year old) approaches in data science and AI;

2

Explain these different approaches and the questions they address;

3

Critically evaluate a range of modern data science approaches;

Discipline Specific Skills and Knowledge:

4

Identify and implement, with limited guidance, appropriate data science and AI methodologies for solving a range of data-rich problems;

5

Synthesise research-informed examples from the literature into novel written work;

Personal and Key Transferable/ Employment Skills and Knowledge:

6

Communicate effectively arguments and conclusions based on evidence gathered from data analysis and AI, using a variety of formats appropriate to the intended audience;

7

Reflect effectively and independently on learning experiences and evaluate personal achievements.

 

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

The module is structured in five blocks of two weeks in which a specific research paper or dataset and related methodologies and approaches are considered in detail. You will work with the most up to date data science and artificial intelligence research, so the syllabus is by design variable. Exemplar topics include:

  • Bayesian Inference
    • Parameter estimation and uncertainty quantification;
    • Bayesian hierarchical models, Bayesian networks;
  • Deep Learning
    • Time series – univariate and multivariate;
    • Image and video analysis, computer vision;
    • Generative deep learning ;
  • Reinforcement Learning
    • Dynamic programming, closed loop optimal management strategy, classical methods;
    • Deep reinforcement learning;
  • Application areas:
    • Geospatial data and remote sensing;
    • Bioinformatics and omics data for living systems;
    • Marine and terrestrial video analysis and animal behaviour tracking;
    • Pollution, microbiological samples: microscopic data, chemical measurements, satellite imaging;
    • Renewable energy generation and consumption data.
LEARNING AND TEACHING
LEARNING ACTIVITIES AND TEACHING METHODS (given in hours of study time)
Scheduled Learning & Teaching Activities 30.00 Guided Independent Study 120.00 Placement / Study Abroad 0.00
DETAILS OF LEARNING ACTIVITIES AND TEACHING METHODS

Category

Hours of study time

Description

Scheduled Learning and Teaching activities

5

Interactive lectures

Scheduled Learning and Teaching activities

25

Computer practicals and discussion sessions

Guided independent study

40

Group work

Guided independent study

40

Methodology and data analyses, report writing and preparation for presentations

Guided Independent Study

40

Self-study and background reading

 

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

Form of Assessment

Size of the assessment e.g. duration/length

ILOs assessed

Feedback method

Draft technical report

1000 words (or equivalent)

1-7

Oral/Written

Practice presentations

2 x 5 minutes

2-4, 6

Oral

 

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

Form of Assessment

 

% of credit

Size of the assessment e.g. duration/length

ILOs assessed

Feedback method

Individual Technical Report

50

3000 words (or equivalent), including a critical appraisal of and reflection about methods and approaches

1-7

Written

Individual Poster Presentation

25

500 words (or equivalent), with a focus on “Why is this method/dataset/approach important and relevant?”

1, 3, 5-7

Oral/Written

Group Presentation

25

15 minutes, addressing the synthesis of methods in an applied context, with a focus on communication

1-2, 4-7

Oral/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

Any

Coursework (100%)

1-7

To be agreed by consequences of failure meeting

 

RE-ASSESSMENT NOTES

Deferral – if you miss an individual assessment for certificated reasons judged acceptable by the Mitigation Committee, you will normally be either deferred in the assessment or an extension may be granted. The mark given for a re-assessment taken as a result of deferral will not be capped and will be treated as it would be if it were your first attempt at the assessment. If you miss a group work assessment for certificated reasons judged acceptable by the Mitigation Committee, you will complete an individual assignment pro-rata (percentage according to the missed component).

Referral – if you have failed the module overall (i.e. a final overall module mark of less than 50%) you will be required to resubmit the original assessment as necessary. The mark given for a re-assessment taken as a result of referral will be capped at 50%.

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

Web-based and electronic resources:

  • ELE – College to provide hyperlink to appropriate pages

Other resources:

  • Recent trends will be made available from current research articles and open source projects on GitHub during the module.

Reading list for this module:

Type Author Title Edition Publisher Year ISBN Search
Set Goodfellow, I., Bengio, Y., Courville, A. and Bengio, Y. Deep learning (Vol. 1, No. 2) MIT Press 2016 [Library]
Set Chollet, F. Deep learning with Python (Vol. 361) Manning 2018 [Library]
Set Davidson-Pilon, C. Bayesian methods for hackers: probabilistic programming and Bayesian inference Addison-Wesley Professional 2015 [Library]
Set Gelman, A., Carlin, J.B., Stern, H.S., Dunson, D.B., Vehtari, A. and Rubin, D.B. Bayesian Data Analysis CRC Press 2013 [Library]
Set Albert, J. Bayesian computation with R Springer 2009 [Library]
Set Sutton, R.S. and Barto, A.G. Reinforcement Learning: An Introduction MIT Press 2018 [Library]
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 Monday 14 December 2020 LAST REVISION DATE Thursday 17 June 2021
KEY WORDS SEARCH Applied data science; Machine learning; Artificial intelligence; Research