Data Mining and Exploration (P00866)

? Credit Points : 10 ? SCQF Level : 11 ? Acronym : INF-P-DME

The aim of this course is to discuss modern techniques for analyzing, interpreting, visualizing and exploiting the data that is captured in scientific and commercial environments. The course will develop the ideas taught in the modules Learning from Data 1 and Probabilistic Modelling and Reasoning and discuss the issues in applying them to real-world data sets, as well as teaching about other techniques and data-visualization methods. The course will also feature case-study presentations and each student will undertake a mini-project on a real-world dataset.

Entry Requirements

? Pre-requisites : Learning from Data (Level 10/11) Probabilistic Modelling & Reasoning For Informatics PG students only, or by special permission of the School.

Subject Areas

Home subject area

Bioinformatics, (School of Informatics, Schedule O)

Other subject areas

Parallel and Distributed Computing, (School of Informatics, Schedule O)

Learning from Data, (School of Informatics, Schedule O)

Delivery Information

? Normal year taken : Postgraduate

? Delivery Period : Semester 2 (Blocks 3-4)

? Contact Teaching Time : 3 hour(s) per week for 10 weeks

First Class Information

Date	Start	End	Room	Area	Additional Information
08/01/2008	14:00	14:50	Lecture Theatre 1, Appleton Tower	Central

All of the following classes

Type	Day	Start	End	Area
Lecture	Tuesday	14:00	14:50	Central
Lecture	Friday	14:00	14:50	Central

Summary of Intended Learning Outcomes

-Describe the data mining process in overview, and demonstrate assessment of the challenges of a given data mining project.
-Describe methods used for exploratory data analysis, predictive modelling and performance evaluation.
-Critical evaluation of papers presented in the second part of the course.
-In the mini-project, demonstrate the ability to conduct experimental investigations and draw valid conclusions from them.
-Demonstrate use of data mining packages/computational environments such as weka and netlab in the mini-project phase.