COMP61011 : Foundations of Machine Learning

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Maintained by G.Brown

Overview

The course unit is assessed 50% by exam, 50% by coursework.

All this information, and more, is available in the course unit handbook.

Exam (50%)

You have a 2 hour, closed-book exam in January. Calculators will be required.
The exam is a mixture of multiple choice, long-answer, and calculation-based questions.
Practice multiple choice questions.
January 2013 exam paper
January 2012 exam paper
January 2011 exam paper

Team Project (50%)

The project work will be in pairs. You will investigate a Machine Learning paradigm/algorithm(s) of your choice. Details are in the course unit handbook, linked above. Some previous good projects are below, to help you see the style/standard expected.
An Experimental Survey of Simple k-Nearest Neighbour Condensing and Editing Algorithms
Comparison of preprocessing techniques and feature selection algorithms in cancer datasets
Implementation and Evaluation of a Random Forest
Implementation of Active Shape Models
Investigation of SVMs for multi-class problems
An Analysis of Feature Selection Techniques
Reducing Dimensionality for Face Recognition