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MA930-15 Data Analysis and Machine Learning

Department
Warwick Mathematics Institute
Level
Taught Postgraduate Level
Module leader
Robin Thompson
Credit value
15
Module duration
5 weeks
Assessment
20% coursework, 80% exam
Study location
University of Warwick main campus, Coventry
Introductory description

N/A.

Module web page

Module aims

This is a core module for the MSc in Mathematics of Systems. The main aims are to provide the students with a broad knowledge of modern techniques of exploratory data analysis, time series modelling and forecasting, and a short introduction to machine learning.

Outline syllabus

This is an indicative module outline only to give an indication of the sort of topics that may be covered. Actual sessions held may differ.

Basic probability: distributions, characteristic functions
Basic statistics: sample mean and variance, law of large numbers and central-limit theorem
Frequentist statistics: point estimation, confidence integrals, type-I and II errors, hypothesis tests
Bayesian statistics: likelihood, maximum likelihood, Bayes theorem, conjugate priors, credible intervals
Time-series analysis: Autocovariance, parameter inference using time-series, time-series forecasting
Machine-learning approaches to data analysis

Learning outcomes

By the end of the module, students should be able to:

  • By the end of this module, the students will be able to quantitatively summarise and critically assess data from real-world systems.
  • By the end of this module, the students will be able to use modern methods of parameter estimation to model and forecast time-series data.
Indicative reading list

G. R. Grimmett and D. R. Stirzaker, Probability and Random Processes (3rd edition, OUP, 2001)
J. R. Norris, Markov Chains (CUP, 1997)
M. J. Keeling and P. Rohani, Modeling infectious diseases in humans and animals (Princeton University Press, 2007)
C.M. Bishop, Pattern Recognition and Machine Learning, Springer 2006
J.D. Hamilton, Time Series Analysis, Princeton University Press 1994
G.E.P. Box, G.M. Jenkins and G.C. Reisel, Time Series Analysis: Forecasting and Control, Wiley 2016 (fifth ed.). Available as an e-book through the Library.

View reading list on Talis Aspire

Subject specific skills

See learning outcomes.

Transferable skills

Students will acquire key reasoning and problem solving skills which will empower them to address new problems with confidence.

Study time

Type Required
Lectures 10 sessions of 2 hours (13%)
Tutorials 10 sessions of 2 hours (13%)
Private study 110 hours (73%)
Total 150 hours
Private study description

Self-study and preparation for exam.

Costs

No further costs have been identified for this module.

You do not need to pass all assessment components to pass the module.

Assessment group D1
Weighting Study time
Assessed Coursework 20% 33 hours
Written Examination 40% 58 hours 30 minutes

Examination may be delivered online depending on local and national restrictions in place at the time as a result of the COVID-19 pandemic.

Oral Examination 40% 58 hours 30 minutes

Vivas may be delivered online depending on local and national restrictions in place at the time as a result of the COVID-19 pandemic.

Feedback on assessment

Written feedback on written assignments plus informal oral feedback during classwork sessions.
Oral feedback on the oral examination.
Written feedback on the written examination.

Past exam papers for MA930

Courses

This module is Core for:

  • Year 1 of RMAA-G1PG Postgraduate Research Mathematics of Systems
  • TMAA-G1PF Postgraduate Taught Mathematics of Systems
    • Year 1 of G1PF Mathematics of Systems
    • Year 1 of G1PF Mathematics of Systems

This module is Optional for:

  • Year 2 of TPXA-F345 Postgraduate Taught Modelling of Heterogeneous Systems (PGDip)

This module is Option list B for:

  • Year 1 of TPXA-F345 Postgraduate Taught Modelling of Heterogeneous Systems (PGDip)