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Faculty of Science Handbook, Session 2017/2018

SIV 3008 INTRODUCTION TO GEOGRAPHIC supervised and unsupervised learning, clustering
INFORMATION SYSTEMS techniques, evaluation models and applications of machine
learning and data mining in bioinformatics. Software tools
This course provides an introduction to the theory and such as MATLAB or WEKA will be introduced and used in
practice of geographic information systems (GIS). This solving bioinformatics problems.
course will introduce some of the basic concepts of GIS,
input of data, storage and management of data and Assessment Methods:
modelling output from GIS. Concepts such as how to model Continuous Assessment: 40%
the complex real world in a computer and the difference Final Examination: 60%
between data and geographic data are covered. Lectures
cover the basics of GIS, vector and raster data models, Medium of Instruction:
geographic data analysis, visualisation techniques and English
geographic overlay. The practical sessions build basic skills
in GIS such as adding, visualising, analysing and modelling Soft Skills:
data and creating effective map layouts. CS3, CT3, TS2
Assessment Methods: Main References:
Continuous Assessment: 60% 1. Jiawei Han and Micheline Kamber (2012). Data Mining:
Final Examination: 40% Concepts and Techniques. Morgan Kaufmann
Publishers.
Medium of Instruction: 2. Ian H. Witten, Eibe Frank, Mark A. Hall (2011). Data
English Mining: Practical Machine Learning Tools and
Techniques. The Morgan Kaufmann Publishers.
Soft Skills: 3. Pang-Ning Tan, Michael Steinbach, Vipin Kumar (2012).
CT3, LL2 Introduction to Data Mining. Addison-Wesley.
Main References:
1. Ian Heywood ,Sarah Cornelius (2011). An Introduction SIV 3012 COMPUTATIONAL INTELLIGENCE IN
to Geographical Information Systems. 4 ed. Prentice BIOINFORMATICS
th
Hall.
2. John R Jensen, Ryan R. Jensen (2013). Introductory This course introduces computational intelligent (CI)
Geographic Information Systems. Pearson. techniques including artificial neural network, fuzzy logic,
3. Keith C. Clarke (2011). Getting Started with genetic algorithm, support vector machine and others.
th
Geographic Information Systems, 5 ed. Prentice Hall. Example of case studies which applied CI in biological
problems will be discussed. Software tools such as
MATLAB will be used to develop and implement the CI
SIV 3009 INTERNET PROGRAMMING models.
This course aims to introduce the World Wide Web (WWW), Assessment Methods:
web software, connections and hardware, introduction to Continuous Assessment: 40%
internet programming and scripting, website maintenance Final Examination: 60%
and Web applications. It covers an introduction to internet
programming and the languages. Topics include HTML, Medium of Instruction:
XHTML, DHTML, XML, CSS, JavaScript, PHP, CGI, Perl, English
and SQL. Besides that, the basic concepts of WWW client- Soft Skills:
server communications and the skills to use the above tool CS3, CT3, TS2
to create Web applications will be also introduced. Various
problems related to Bioinformatics such as query, search Main References:
and retrieve information are considered to be solved using 1. Andres P. Engelbrecht (2014). Computational
internet programming languages. Intelligence: An Introduction. Wiley.
2. Russ Eberhart and Yuhui Shi (2014). Computational
Assessment Methods: Intelligence: Concepts to Implementations. Morgan
Continuous Assessment: 60% Kaufmann.
Final Examination: 40% 3. S. Sumathi, Surekha Paneerselvam (2010).
Computational Intelligence Paradigms: Theory and
Medium of Instruction: Applications using MATLAB. CRC Press.
English
Soft Skills: SIV 3013 SOFTWARE ENGINEERING IN
CS3, CT3, TS2 BIOINFORMATICS

Main References: The syllabus of this course includes basic concepts of
1. Scott Guelich, Shishir Gundavaram, Gunther Birznieks software engineering, requirements gathering, requirement
(2012). CGI Programming with Perl. 2 nd Ed. O’Reilly specification and analysis, implementing systems, coding
Media. style and writing manuals.
2. Mitchell L Model (2013). Bioinformatics Programming
Using Python. O’Reilly Media. Assessment Methods:
3. Ethan Cerami (2013). XML for Bioinformatics. Springer. Continuous Assessment: 60%
Final Examination: 40%
SIV 3010 DATA MINING AND MACHINE LEARNING Medium of Instruction:
English
This course introduces basic conceptual elements of
machine learning and data mining including data Soft Skills:
preprocessing methods, classification techniques, CT3, TS2, LL2

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