CM50268 - Bayesian Machine Learning - Engineering Assignment Help

Assignment Task

Overview
• Submission type: project report + your own Python code

The final piece of assessed coursework involves the evaluation of Bayesian modelling methods on a real multivariate regression task. The guiding objectives are to derive a good predictor for data derived from an “energy efficiency” data set, and to estimate which of the input variables are relevant for prediction.
In particular, the exercise focuses on approximating (and averaging over) posterior distributions using the Hamiltonian Monte Carlo stochastic method (an implementation of this algorithm is supplied). Your experiments will be based mainly on existing (or supplied) analytic code and techniques you have already applied. You will of course need to write all the relevant code to process the data, apply the methods appropriately, extend them in places, and ultimately calculate and output the necessary results.

A key part of the assessment is to compile, present and critique all those results effectively within a “project report” document. For this exercise, your code will not be assessed; marks will be awarded based solely on the content of the report.

Data
You will be analysing the “Energy efficiency” data set, originally from the University of Oxford, and now made available at the UCI Machine Learning Repository.
This multivariate data set contains 768 examples and comprises eight input variables x1, x2, · · · , x8 presenting some basic architectural parameters for buildings (e.g. “Roof Area” and “Glazing Area”) with the intention of predicting a ninth target variable y, the required “Heating Load”. This can be considered a real-value variable, suitable for standard regression modelling (with the usual Gaussian noise model).
The data has been pre-processed and equally split into two specific data sets for your use:
• Training set: ee-train.csv
• Test set: ee-test.csv
Both csv files have a header row (labelling each variable), with the first 8 columns representing the input variables (x1, x2, · · · , x8) and the final column being the target variable (y “Heating Load”). For the purposes of modelling, you may find it useful to:
• Add a “bias” input (a constant) to your models
• Standardise the other inputs to mean zero and standard deviation one
For model training, only ee-train.csv should be used, and ee-test.csv should be reserved purely for assessing model performance.

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