Module Descriptor School of Computer Science and Statistics
Module Code | CS7DS3 |
Module Name | Applied Statistical Modelling |
Module Short Title | |
ECTS | 5 |
Semester Taught | HT (2nd Semester) |
Contact Hours | 2 lecture/lab hours per week |
Module Personnel | Assistant Professor Arthur White |
Learning Outcomes | Students who complete this module should be able to:
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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 Data | 2017/18 |