Current postgraduate taught students
COMP61011: Machine Learning and Data Mining (2010-2011)
Machine learning is concerned with creating mathematical "data structures" that allow a computer to exhibit behaviour that would normally require a human. Typical applications might be spam filtering, speech recognition, medical diagnosis, or weather prediction. The data structures we use (known as "models") come in various forms, e.g. trees, graphs, algebraic equations, probability distributions. The emphasis is on constructing these models automatically from data---for example making a weather predictor from a datafile of historical weather patterns. This course will introduce you to the concepts behind various Machine Learning techniques, including how they work, and use existing software packages to illustrate how they are used on data. The course has a fairly mathematical content although it is intended to be self-contained.
This course unit aims to introduce the main algorithms used in modern machine learning, to introduce the theoretical foundations of machine learning and to provide practical experience of applying machine learning techniques.
|Programme outcome||Unit learning outcomes||Assessment|
|G1||Have knowledge and understanding of the principle algorithms used in modern machine learning, as outlined in the syllabus.|
|G1||Have sufficient knowledge of information theory and probability theory to understand some basic theoretical results in machine learning.|
|G3||Be able to apply machine learning algorithm to real datasets, evaluate their performance and appreciate the practical issues involved.|
|G4||Be able to provide a clear and concise description and justification for the employed experimental procedures.|
- Classifiers and the Nearest Neighbour Rule
- Linear Models, Support Vector Machines
- Decision Trees, Feature Selection, Mutual Information
- Probabilistic Classifiers and Bayes Theorem
- Combining Models - ensemble methods, mixtures of experts, boosting
- Algorithm assessment - overfitting, generalisation, comparing two algorithms
Write a research paper applying appropriate techniques on supplied datasets.