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://course.inf.ed.ac.uk/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. |
Information for Visiting Students
| Pre-requisites | None |
| Displayed in Visiting Students Prospectus? | Yes |
Course Delivery Information
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| Delivery period: 2013/14 Semester 2, Available to all students (SV1)
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Learn enabled: No |
Quota: None |
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Web Timetable |
Web Timetable |
| Course Start Date |
13/01/2014 |
| Breakdown of Learning and Teaching activities (Further Info) |
Total Hours:
100
(
Lecture Hours 20,
Supervised Practical/Workshop/Studio Hours 4,
Summative Assessment Hours 2,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
72 )
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| Additional Notes |
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| Breakdown of Assessment Methods (Further Info) |
Written Exam
50 %,
Coursework
50 %,
Practical Exam
0 %
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| 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 |
The Elements of Statistical Learning; Data Mining, Inference and Prediction, Hastie, Tibshirani and Friedman
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| 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 Iain Murray
Tel: (0131 6)51 9078
Email: I.Murray@ed.ac.uk |
Course secretary | Ms Katey Lee
Tel: (0131 6)50 2701
Email: Katey.Lee@ed.ac.uk |
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© Copyright 2013 The University of Edinburgh - 13 January 2014 4:27 am
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