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DEGREE REGULATIONS & PROGRAMMES OF STUDY 2011/2012

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DRPS : Course Catalogue : School of Informatics : Informatics

Postgraduate Course: Data Mining and Exploration (INFR11007)

Course Outline
School School of Informatics College College of Science and Engineering
Course type Standard Availability Available to all students
Credit level (Normal year taken) SCQF Level 11 (Postgraduate) Credits 10
Home subject area Informatics Other subject area None
Course website http://www.inf.ed.ac.uk/teaching/courses/dme Taught in Gaelic? No
Course description 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 Students MUST have passed: Probabilistic Modelling and Reasoning (INFR11050)
It is RECOMMENDED that students have passed Introductory Applied Machine Learning (INFR09029)
Co-requisites Students MUST also take: Machine Learning & Pattern Recognition (Level 10) (INFR10036) OR Machine Learning & Pattern Recognition (Level 11) (INFR11073)
Prohibited Combinations Other requirements For Informatics PG students and final year MInf students only, or by special permission of the School.
Additional Costs None
Information for Visiting Students
Pre-requisites None
Displayed in Visiting Students Prospectus? Yes
Course Delivery Information
Delivery period: 2011/12 Semester 2, Available to all students (SV1) WebCT enabled:  No Quota:  None
Location Activity Description Weeks Monday Tuesday Wednesday Thursday Friday
CentralLecture1-11 14:00 - 15:50
First Class First class information not currently available
Exam Information
Exam Diet Paper Name Hours:Minutes
Main Exam Diet S2 (April/May)2:00
Summary of Intended Learning Outcomes
1 - Describe the data mining process in overview, and demonstrate assessment of the challenges of a given data mining project
2 - Describe methods used for exploratory data analysis, predictive modelling and performance evaluation
3 - Critical evaluation of papers presented in the second part of the course
4 - In the mini-project, demonstrate the ability to conduct experimental investigations and draw valid conclusions from them
5 - Demonstrate use of data mining packages/computational environments such as weka and netlab in the mini-project phase
Assessment Information
Written Examination 50
Assessed Assignments 35
Oral Presentations 15

Assessment
Two items, (1) the presentation of research paper on data mining to the class and (2) a mini-project on one dataset chosen from a list of datasets selected by the instructor.

If delivered in semester 1, this course will have an option for semester 1 only visiting undergraduate students, providing assessment prior to the end of the calendar year.
Special Arrangements
None
Additional Information
Academic description Not entered
Syllabus The course will consist of two parts, the first part being a series of lectures on what is outlined below. It is anticipated that there will also be one or two guest lectures from data mining practitioners.

The second part will consist of student presentations of papers relating to relevant topics. Students will also carry out a practical mini-project on a real-world dataset. For both paper presentations and mini-projects, lists of suggestions will be available, but students may also propose their own, subject to approval from the instructor.

* Introduction, overview
* Data preprocessing and cleaning, dealing with missing data
* Data visualization, exploratory data analysis
* Data mining techniques, e.g. Association rules (Apriori algorithm),
* Predictive modelling techniques (e.g. SVMs)
* Performance evaluation (e.g. ROC curves)
* Issues relating to large data sets
* Application areas, e.g. text mining, collaborative filtering, retrieval-by-content, web mining, bioinformatics data, astronomy data

Relevant QAA Computing Curriculum Sections: Artificial Intelligence
Transferable skills Not entered
Reading list * Recommended text: Principles of Data Mining. David J. Hand, Heikki Mannila and Padhraic Smyth, MIT Press (2001)
* Additional texts: Data Mining: Concepts and Techniques. Jiawei Han and Micheline Kamber, Morgan Kaufmann (2001)
Study Abroad Not entered
Study Pattern Lectures 20
Tutorials 0
Timetabled Laboratories 4
Non-timetabled assessed assignments 50
Private Study/Other 26
Total 100
Keywords Not entered
Contacts
Course organiser Dr Michael Rovatsos
Tel: (0131 6)51 3263
Email: mrovatso@inf.ed.ac.uk
Course secretary Miss Kate Weston
Tel: (0131 6)50 2701
Email: Kate.Weston@ed.ac.uk
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copyright 2011 The University of Edinburgh - 3 April 2011 11:20 am