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DRPS : Course Catalogue : School of Informatics : Informatics

Postgraduate Course: Data Mining and Exploration (INFR11007)

Course Outline
SchoolSchool of Informatics CollegeCollege of Science and Engineering
Credit level (Normal year taken)SCQF Level 11 (Postgraduate) AvailabilityAvailable to all students
SCQF Credits10 ECTS Credits5
SummaryThe 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.
Course description 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
Entry Requirements (not applicable to Visiting Students)
Pre-requisites It is RECOMMENDED that students have passed Introductory Applied Machine Learning (INFR09029)
Co-requisites Students MUST also take: Machine Learning & Pattern Recognition (Level 11) (INFR11073) AND Probabilistic Modelling and Reasoning (INFR11050)
Prohibited Combinations Other requirements This course is open to all Informatics students including those on joint degrees. For external students where this course is not listed in your DPT, please seek special permission from the course organiser.
Information for Visiting Students
Course Delivery Information
Academic year 2014/15, Available to all students (SV1) Quota:  None
Course Start Semester 2
Timetable Timetable
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 )
Assessment (Further Info) Written Exam 50 %, Coursework 50 %, Practical Exam 0 %
Additional Information (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.

You should expect to spend approximately 50 hours on the coursework for this course.

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.
Feedback Not entered
Exam Information
Exam Diet Paper Name Hours & Minutes
Main Exam Diet S2 (April/May)2:00
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
Reading List
The Elements of Statistical Learning; Data Mining, Inference and Prediction, Hastie, Tibshirani and Friedman
Additional Information
Course URL
Graduate Attributes and Skills Not entered
KeywordsNot entered
Course organiserDr Nigel Goddard
Tel: (0131 6)51 3091
Course secretaryMs Katey Lee
Tel: (0131 6)50 2701
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