Data Mining and Exploration: Module Homepage

This is a course for MSc level students that runs in semester 2. The course descriptor can be found here.

Important: PMR and MLPR are co-requisites for this course.

Lectures

Lectures: Friday 14:00-15:50

Lab Class

You are expected to work through the lab material in your own time, but during the lab period you can ask questions and get help on the material in the lab tutorials.

The main aim of the labs will be to learn how to start visualising and working on a problem via Weka.

The weka lessons are available here. They start in week 3 and run through to week 6. They are Tues 15:10-17:00. This is a 2-hour session, so feel free to come at the time that suits you. See Theon or email the course's TA Stefanos Angelidis for more information.

Module Resources

Discussion Forum

A web discussion site is available via nb.mit.edu. You should receive an email about this.

Assessed Work

Week-by-Week listing

These are put here for convenience, but are subject to significant changes.


Week 1

Lectures Introduction lecture slides, Visualization lecture slides
Document for discussion.Visualization Notes.


Week 2

Lectures Data Preprocessing lecture slides, Descriptive Modelling lecture slides


Week 3

Lectures Descriptive Modelling continued. Predictive Modelling 1 lecture slides
Handout SVM Notes


Week 4

Lectures Predictive Modelling II lecture slides.
Association Rules lecture slides, Web resources ROC Graphs: Notes and Practical Considerations for Data Mining Researchers by Tom Fawcett, Technical report HPL-2003-4.


Week 5

Mining Complex Data lecture slides. Presentation on Visualization of Navigation Patterns on a Web Site Using Model Based Clustering (2000) by I. Cadez et al, lecture slides
Issue in handing data.


Weeks 6-10 Student Presentations (not up to date - ignore)

Week 6: Tuesday - No lecture

Week 6: Friday

Alfredo Kalaitsis and Konstantina Palla. Robust Real-Time Face Detection.
Papanikolaou Amalia and Makrymallis Antonios - Discovery of Climate Indices using Clustering

Week 7: Tuesday

Dimitrios Milios, Anastasios Polymeros - Optimizing Search Engines Using Clickthrough Data

Week 7: Friday

Giulio Meneghin, Javier Kreiner - The boosting approach to machine learning: An overview
Dan Harvey & Sean Moran - Matching Words and Pictures

Week 8: Tuesday

Nicolas Greffard & Stephane Clery - Using Bayesian networks to analyze expression data
Andreas Damianou, Ioannis Pavlopoulos - Probabilistic Latent Semantic Indexing

Week 8: Friday

Philipp Petrenz, Srikanth Sundaram, Avinash Ranganath - Web document clustering: A feasibility demonstration
Pavel Petchko - Collaborative Ensemble Learning

Week 9: Tuesday

Cen Zhe Qiao & Zhen Wei He - Mining the network value of customer
Xudong He & Ailun Yi - Hierarchical Classification of Web Content

Week 9: Friday

Aciel Eshky & Marco Brigham. Object Recognition with Informative Features and Linear Classification
Summary and Discussion

This page is maintained by Amos Storkey



Home : Teaching : Courses 

Informatics Forum, 10 Crichton Street, Edinburgh, EH8 9AB, Scotland, UK
Tel: +44 131 651 5661, Fax: +44 131 651 1426, E-mail: school-office@inf.ed.ac.uk
Please contact our webadmin with any comments or corrections. Logging and Cookies
Unless explicitly stated otherwise, all material is copyright © The University of Edinburgh