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Study-Unit Description
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CODE ICS2201

 
TITLE Search and Optimisation Techniques

 
LEVEL 02 - Years 2, 3 in Modular Undergraduate Course

 
ECTS CREDITS 5

 
DEPARTMENT Intelligent Computer Systems

 
DESCRIPTION This study-unit will introduce techniques in Search and Optimisation and includes an introduction to Inductive, Deductive, Supervised and Unsupervised Learning, typical problems that need approximate solutions and can be constructed as search problems, Vector Quantisation and Clustering, Combinatorial Optimisation, Monte Carlo Methods, Genetic Algorithms, Simulated Annealing, Ant Colony Optimisation, and an introduction to Markov techniques.

Study-unit Aims

- Introduce the students to the area.
- Understand different algorithms that can be used to solve typical problems.
- Introduce the theoretical foundations of search and optimisation problems.
- Enable the student to choose algorithms to solve problems.
- Prepare students for a unit in Machine Learning.

Learning Outcomes

1. Knowledge & Understanding: By the end of the study-unit the student will be able to:

- Analyse and determine how and why stochastic and approximate methods are required to solve certaint types of problems.
- Implement Search and Optimisation algorithms.
- Identify problems can be tackled using these algorithms.

2. Skills: By the end of the study-unit the student will be able to:

- Use Search and Optimisation techniques to solve real-world problems.
- Choose the right algorithms to solve problems which may be intractable using other methods or ones that are poorly defined.

Main Text/s and any supplementary readings

Notes and references given in class.

 
ADDITIONAL NOTES Pre-requisite Study-unit: CSA1017

 
STUDY-UNIT TYPE Lecture and Independent Study

 
METHOD OF ASSESSMENT
Assessment Component/s Resit Availability Weighting
Assignment Yes 30%
Examination (2 Hours) Yes 70%

 
LECTURER/S John M. Abela
Alexiei Dingli
Kristian Guillaumier

 
The University makes every effort to ensure that the published Courses Plans, Programmes of Study and Study-Unit information are complete and up-to-date at the time of publication. The University reserves the right to make changes in case errors are detected after publication.
The availability of optional units may be subject to timetabling constraints.
Units not attracting a sufficient number of registrations may be withdrawn without notice.
It should be noted that all the information in the study-unit description above applies to the academic year 2017/8, if study-unit is available during this academic year, and may be subject to change in subsequent years.
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