ID5059: Machine Learning for Data Analysis

Learning Outcomes

• Understand the mathematics underpinning common machine-learning/data-mining methods, including parameter estimation
• Determine what models are applicable for different data and objectives
• Understand complex regressions from the perspective of basis functions, tree methods, boosting/bagging/ensemble model variants, neural networks, deep-learning, and other selected method
• Conduct hyperparameter-tuning/model-selection as appropriate to the model
• Manipulate data, fit models, and summarise/display their results/performance and objectively compare models in a suitable language
• Conduct comprehensive analysis of large real-world data, within a group, covering: data preparation; model fitting, critique & refinement; and presentation of results to a range of audiences

Syllabus

Contemporary data collection can be automated and on a massive scale e.g. credit card transaction databases. Large databases potentially carry a wealth of important information that could inform business strategy, identify criminal activities, characterise network faults etc. These large scale problems may preclude the standard carefully constructed statistical models, necessitating highly automated approaches. This module covers many of the methods found under the banner of “Datamining”, building from a theoretical perspective but ultimately teaching practical application. Topics covered include: historical/philosophical perspectives, model selection algorithms and optimality measures, tree methods, bagging and boosting, neural nets, and classification in general. Practical applications build sought-after skills in programming.

Compulsory Elements

This module has no compulsory elements beyond those common to all modules (mark of 4 in each assessment component).

Links

• Module catalogue entry
• Reading list
• Resources

Module Delivery

• Peter Macgregor
• Maths Lecturer TBC