COMPUTING SCIENCE
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University | Computing Science | Modules | CSCU9T6 | Home Updated 10/05/16 11:05
CSCU9T6 Information Systems Spring 2017

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Materials

Assessment

Reference

 

CSCU9T6 Syllabus

Credits

20 credits at SCQF level 10

Undergraduate Course

Prerequisites

None

Learning Outcomes

The student should know and understand:

  • The importance of data in organisations, identifying the difference between data and information
  • The concept of data mining
  • Different techniques that can be used to mine data, with particular emphasis given to the use of Bayesian belief networks
  • The role of graphics and visualisation in data mining and data representation
  • How reasoning processes can be implemented, to extend stored knowledge
  • Different types of information systems and the methods they adopt for knowledge discovery

Transferable Skills

  • Understanding of how data is transformed into information
  • Good knowledge of information systems in terms of technologies used and their applicability for different tasks
  • Knowledge of key data mining techniques

Contents

Bayesian Belief Networks

  • Understanding the use of probability information in predicting data values
  • Development of Bayesian belief network models
  • Hidden Markov models

Data Mining

  • An introduction to data mining
  • Market analysis and machine learning.
  • Statistical and other techniques for data mining.
  • Tailoring information systems.
  • Importance of data visualization
  • Data warehousing concepts

Reasoning Systems

  • Data, information, knowledge
  • Rule-based systems
  • Uncertainty: fuzzy logic, certainty factors
  • Case based reasoning
Students should also be able to demonstrate the ability to apply theory and techniques to unseen problems without reference to notes, to work independently and under a time constraint.

Assessment

One assignment worth 50%.
One exam worth 50%

Textbooks

Data Mining: Practical Machine Learning Tools and Techniques, I.H. Witten and E. Frank. 2nd Edition. Morgan Kaufmann, 2005.

Data Mining Techniques: for Marketing, Sales, and Customer Relationship Management (1st ed 1997 or 2nd ed 2004), MJA Berry and GS Linoff, Wiley (background).

Bayesian Artificial Intelligence, KB Korb and AE Nicholson, Chapman and Hall/CRC, 2004 (background).

Artificial Intelligence: A Guide to Intelligent Systems, 3rd edition (2011), Michael Negnevitsky, ISBN: 1408225743, Addison Wesley (background reading)

Requirements

In order to obtain a pass grade for the unit you must:
  • Submit all items of assessed coursework
  • Attend the examination.

Non-submission of the assessed coursework will result in the award of an X Grade for the module as a whole. Assessed coursework submitted late will be accepted up to seven calendar days after the submission date (or expiry of any agreed extension) but the grade will be lowered by three marks per day or part thereof. After seven days the piece of work will be deemed a non-submission, and will result in a fail grade for the module as a whole. This rule (regarding coursework) may be relaxed for students who can show good cause for failure to submit. Good cause may include illness (for which a medical certificate or other evidence will be required).

If a student is unable to attend the Main examination, he/she must apply to the Student Programmes Office for a Deferred examination. If a Deferred examination is not granted, then the Examiners may allow a Repeat examination.

Assessment guidelines can be found here

Extenuating circumstances

Students may encounter personal difficulties outwith their control that affect their ability to study or complete assessments. In most cases, these situations can be handled by requesting extensions of deadlines or deferred exams. In exceptional cases where these remedies cannot be applied, the student may wish to make a request for consideration of extenuating circumstances. Requests can be made using the Extenuating Circumstances form. Note that the form must be submitted no later than two working days after the final assessment for the module.

Attendance

You are expected to attend all lectures, tutorials, and practical classes, in order to derive the maximum benefit from your time at University. It is your responsibility to make the most of the opportunities for education offered to you by the University.

Note that lectures are recorded, so you may also be recorded if you speak while the recording is in process. Recordings are published online and are publicly available.

Plagiarism

Plagiarism means presenting the work of others as though it were your own. The University takes a very serious view of plagiarism, and the penalties can be severe (ranging from a reduced grade in the assessment, through a fail grade for the module, to expulsion from the University for more serious, or repeated, offences). See the University guidelines on this at http://www.quality.stir.ac.uk/ac-policy/Misconduct.php

Handbook

You will receive a copy of the Computing Science student handbook. You should read this carefully, particularly the sections on assessment and plagiarism. There is also useful information in there about course structure, which will help you plan your future module choices in Computing. The handbook is also available online at http://www.cs.stir.ac.uk/courses/ug-handbook.pdf
     
Coordinator Andrea Bracciali, Room 4B86
Email abb@cs.stir.ac.uk - Tel 01786 467446 - Fax 01786 464551
Mail Computing Science and Mathematics, University of Stirling, Stirling, Scotland, FK9 4LA
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