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Module Descriptor School of Computer Science and Statistics

Module CodeCS7DS3
Module NameApplied Statistical Modelling
Module Short Title
Semester TaughtHT (2nd Semester)
Contact Hours

2 lecture/lab hours per week

Module PersonnelAssistant Professor Arthur White
Learning Outcomes

Students who complete this module should be able to:

  • DS3LO1 Define a Markov chain and describe its theory
  • DS3LO2 Identify an appropriate Monte Carlo simulation method for a given probability distribution and implement it.
  • DS3LO3 Describe and implement the state of the art methodology in several topical applications in data science.
  • DS3LO4 Complete a data science project that applies the methods of this and other modules to a real data set.
Learning Aims

This module continues on from CS7CS4 (Machine Learning) with a focus on sampling methods and topical applications.  It also gives an opportunity for students to apply, through a large project, the methods that they have explored in CS7DS1 (Data Mining & Analytics) and that they are currently exploring in CS7DS2 (Optimisation Algorithms for Data Analysis).

Module Content

Markov chains;

Monte Carlo sampling methods;

Hierarchical graphical models;

Introduction to databases: MySQL, and tidyr.

Project: application of statistical and machine learning methods to real data example.

Recommended Reading List

Bishop, C.M., “Pattern Recognition and Machine Learning,” Springer-Verlag New York, 2006.

Murphy, Kevin P., “Machine Learning: A Probabilistic Perspective,“ MIT Press, 2013.

Wood, S. “Core Statistics,” Cambridge University Press, 2016.

Module Prerequisites
Assessment Details

Coursework: 100%

30% of the coursework mark will be allocated to smaller assignments and 70% to a larger-scale project to be handed in at end of module.

Assessment in the Supplemental session will be based on 100% coursework.

Module Website
Academic Year of Data2017/18