Postgraduate Course: Soft Computing (CMSE11448)
Course Outline
| School | Business School |
College | College of Arts, Humanities and Social Sciences |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
Availability | Not available to visiting students |
| SCQF Credits | 10 |
ECTS Credits | 5 |
| Summary | Real life decision problems are often too complicated to be modelled by e.g., mathematical tools. Even if they are modelled, these type of problems are often intractable and extremely challenging to solve. In recent years, the emergence of soft computing as an alternative way of solving problems in areas such as optimisation has attracted attentions from both academics and practitioners. This course offers alternative approaches to solve complex problems which could otherwise be difficult to solve by traditional techniques. It aims at training students in the field of Soft Computing with emphasis on uncertainty modeling (e.g., Bayesian Networks) and approximation (e.g., heuristics, metaheuristics ,hyperheuristics and evolutionary computations) to address decision making problems in business. Variety of applications will be examined including transportation, logistics and fleet management. The course further aim is to enhance students understanding of the critical nature of designing and/or selecting appropriate methods for solving complex decision problems. It provides opportunities for students to learn from each other, from practitioners in the field, and from the latest theoretical and applied research in the field. |
| Course description |
This course has been designed around two main topics of approximation techniques and Bayesian modelling. At the beginning of the course, a project will be introduced in the area of location analysis or logistics. Students are expected to work on the project individually and as a member of a group during the term time.
Outline Content
The first part of the course begins with an introduction to soft computing followed by a brief discussion on basic approximation techniques such as CLS. Built upon this introduction, the advanced techniques e.g., meta-heuristics, evolutionary computation and hyper-heuristics will be discussed in the following weeks. In the second part pf the course, Bayesian Networks (BNs) and its applications to Business Analytics are discussed. The focus will be on how to design a BN and how to perform inference analysis on a hypothesis of interest.
Student Learning Experience
Students are expected to learn basic concepts and theories from lectures. Working on their individual and group assignment during the term time, students will learn how to apply the concepts and theories learned in the lectures to solve a complex business problem such as the one described in their assignment brief.
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
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Co-requisites | |
| Prohibited Combinations | |
Other requirements | None |
Course Delivery Information
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| Academic year 2024/25, Not available to visiting students (SS1)
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Quota: None |
| Course Start |
Block 3 (Sem 2) |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
100
(
Lecture Hours 10,
Seminar/Tutorial Hours 5,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
83 )
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| Assessment (Further Info) |
Written Exam
0 %,
Coursework
100 %,
Practical Exam
0 %
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| Additional Information (Assessment) |
80% coursework (individual) - assesses all course Learning Outcomes
20% coursework (group) - assesses all course Learning Outcomes
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| Feedback |
Formative: Feedback will be provided throughout the course.
Summative: Feedback will be provided on the assessment within agreed deadlines. |
| No Exam Information |
Learning Outcomes
On completion of this course, the student will be able to:
- Critically discuss and express the concept and methods of soft computing using proper terminologies.
- Analyse decision problems in business settings using soft computing techniques.
- Implement a soft computing technique, interpret results and formulate managerial guidelines and make recommendations.
- Communicate findings effectively and efficiently verbally and in writing.
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Learning Resources
Recommended Journals:
European Journal of Operational Research
INFORMS journal on Computing
Transportation Science
Computers and Operations Research
Transportation Research-Part B
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Additional Information
| Graduate Attributes and Skills |
Communication, ICT, and Numeracy Skills
After completing this course, students should be able to:
Critically evaluate and present digital and other sources, research methods, data and information; discern their limitations, accuracy, validity, reliability and suitability; and apply responsibly in a wide variety of organisational contexts.
Practice: Applied Knowledge, Skills and Understanding
After completing this course, students should be able to:
Apply creative, innovative, entrepreneurial, sustainable and responsible business solutions to address social, economic and environmental global challenges.
Cognitive Skills
After completing this course, students should be able to:
Be self-motivated; curious; show initiative; set, achieve and surpass goals; as well as demonstrating adaptability, capable of handling complexity and ambiguity, with a willingness to learn; as well as being able to demonstrate the use digital and other tools to carry out tasks effectively, productively, and with attention to
quality.
Knowledge and Understanding
After completing this course, students should be able to:
Demonstrate a thorough knowledge and understanding of contemporary organisational disciplines; comprehend the role of business within the contemporary world; and critically evaluate and synthesise primary and secondary research and sources of evidence in order to make, and present, well informed and transparent organisation-related decisions, which have a positive global impact.
Identify, define and analyse theoretical and applied business and management problems, and develop approaches, informed by an understanding of appropriate quantitative and/or qualitative techniques, to explore
and solve them responsibly.
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| Keywords | Not entered |
Contacts
| Course organiser | Dr Nader Azizi
Tel: (0131 6)51 1491
Email: Nader.Azizi@ed.ac.uk |
Course secretary | Miss Jen Wood
Tel: (0131 6)50 8335
Email: J.Wood@ed.ac.uk |
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