This is an archived syllabus from 2013-2014
COMP61011 Machine Learning and Data Mining syllabus 2013-2014
COMP61011 Machine Learning and Data Mining
Enrolled students: 47
Course leader: Gavin Brown
Additional staff: view all staff
- 50% Written exam
- 50% Coursework
|Sem 1 P1||Lecture||2.19||Wed||09:00 - 09:00||-|
|Sem 1 P1||Lab||2.25b||Wed||13:00 - 13:00||-|
- Learning from Data
- Information Management 1
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.
- To introduce the main algorithms used in modern machine learning.
- To introduce the theoretical foundations of machine learning.
- To provide practical experience of applying machine learning techniques.
If you have sat an undergraduate ML course (particularly my COMP24111) then you may feel you know all this material. In fact we will cover virtually the same topics - however, you almost certainly will not have covered this material in the same depth as we will cover it. We will study why and how these methods work, at a very deep level. This is not a course on how to use ML techniques. It is a course on the foundations, the deeper aspects. If you really think you know it all already, then try sitting the previous exam papers, under exam conditions of course (i.e. no textbooks).
- Classifiers and the Nearest Neighbour Rule
- Linear Models, Support Vector Machines
- Algorithm assessment - overfitting, generalisation, comparing two algorithms
- Decision Trees, Feature Selection, Mutual Information
- Probabilistic Classifiers and Bayes Theorem
- Combining Models - ensemble methods, mixtures of experts, boosting
- Feature Selection - basic methods, plus some tasters of research material
- Write a 6 page research paper applying appropriate ML techniques on supplied datasets.
1 day per week (5 weeks)
Feedback methodsFace to face marking of preliminary work, on a weekly basis, plus written feedback on a larger project.
- Assessment written exam (2 hours)
- Lectures (10 hours)
- Practical classes & workshops (20 hours)
- Analytical skills
- Project management
- Oral communication
- Problem solving
- Written communication
On successful completion of this unit, a student will be able to:
Learning outcomes are detailed on the COMP61011 course unit syllabus page on the School of Computer Science's website for current students.
No reading list found for COMP61011.
Course unit materials
Links to course unit teaching materials can be found on the School of Computer Science website for current students.