Module Descriptor School of Computer Science and Statistics
|Module Name||Applied Statistical Modelling|
|Module Short Title|
|Semester Taught||HT (2nd Semester)|
2 lecture/lab hours per week
|Module Personnel||Assistant Professor Arthur White|
Students who complete this module should be able to:
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).
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.
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.
|Academic Year of Data||2017/18|